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This file is part of the following reference:
Tang, Lei Stanley (2016) Conservation genetics of
granivorous birds in a heterogeneous landscape: the case
of the black-throated finch (Poephila cincta). PhD thesis, James Cook University.
Access to this file is available from:
http://researchonline.jcu.edu.au/48857/
The author has certified to JCU that they have made a reasonable effort to gain
permission and acknowledge the owner of any third party copyright material
included in this document. If you believe that this is not the case, please contact
[email protected] and quote
http://researchonline.jcu.edu.au/48857/
ResearchOnline@JCU
Conservation genetics of granivorous birds in a heterogeneous
landscape: the case of the Black-throated Finch (Poephila
cincta)
Thesis submitted by:
Lei Stanley TANG BSc (Bioinformatics, Suzhou University, China) MAppSc (Zoology, James Cook University, Australia) Submission date August 25th, 2016 For the degree of Doctor of Philosophy In the discipline of Environmental Studies College of Science and Engineering, James Cook University
Every reasonable effort has been made to gain permission and acknowledge the owners of copyright material. I would be pleased to hear from any copyright owner who has been omitted or incorrectly acknowledged.
This thesis is covered under the following permits:
• Scientific Purposes Permit under the Nature Conservation Regulation (2006), Queensland Government, Permit Number: WISP10390011
• Approval for Animal Based Research, Animal Ethics Committee, James Cook University, Approval Number: A1693
• Banding Project Permit, Australian Bird and Bat Banding Scheme, Australian Government, Authority Number: 2876
AUTHOR CONTRIBUTIONS OF PUBLISHED PAPER(S)
Chapter Number III
Publication(s)
Tang, L. S., C. Smith-Keune, M. G. Gardner, and B. D. Hardesty. 2014. Development, characterisation and cross-species amplification of 16 novel microsatellite markers for the endangered Black-throated Finch (Poephila cincta) in Australia. Conservation Genetics Resources 6:143-146
Nature and extent of intellectual input
The authors co-developed the research question. Tang collected the data and performed the data analyses with assistance from Smith-Keune and Gardner. Tang wrote the first draft of the paper which was revised with editorial input from Smith-Keune, Gardner and Hardesty. Tang developed the tables.
I confirm the candidate’s contribution to this paper and consent to the inclusion of the paper in this thesis
C. Smith-Keune
Signature:
M.G. Gardner
Signature:
D.B. Hardesty
Signature:
i
ACKNOWLEDGEMENTS
There are too many people that I want to express my gratitude to during the journey of
my PhD in the past four and half years. First and foremost, I would like to thank my
advisory team: Dr. James Moloney (primary advisor), Dr. Tony Grice, Dr. Denise
Hardesty, Dr. Carolyn Smith-Keune and Professor Peter Valentine. Without their
dedicated professional, financial and personal support, I would have not been able to
complete this project. The consistent understanding and guidance from Tony, James and
Denise ensured that I stayed on track throughout my PhD candidature. They have been
great mentors and taught me how to become a true scientist. Carolyn joined my
advisory team at a later stage, but her exceptional knowledge of molecular biology and
population genetics helped me tremendously in completing the lab work and genetic
data analysis. The joy and enthusiasm she has for her research was contagious and
motivational for me, even during tough times in my PhD pursuit. Peter only stayed on
my advisory board for a short period of time during the first 2 years of my candidature,
but his extensive experience in the field of conservation biology guided me greatly in
finalising the structure of this thesis.
The members of the Black-throated Finch Recovery Team have contributed immensely
during my candidature. The group has been a source of good advice and collaboration. I
am especially grateful to those who provided me with information on where to find the
Black-throated Finch in the wild at the beginning stage of my candidature: Dr. Tony
Grice, Dr. April Reside, Dr. Eric Vanderduys, and Mr. George Baker (former member).
The team has also stood up by my side while negotiating with landholders and property
owners for permissions to access. I would like to acknowledge the sighting records of
the Black-throated Finch in the Townsville region given to me by the team for the
ii
landscape genetics analysis. Without the extensive knowledge and collaborations of
members of the team, my PhD would have not been able to initiate.
In regards to the laboratory analysis, I thank all members of the Molecular Ecology and
Evolutionary Laboratory (MEEL) at James Cook University. They not only gave me
great advice on how to operate all machines in the lab, but also shared their friendships
with me. Particularly, I thank all lab managers that helped me to find my way around in
the lab both currently and in the past. Ms. Blanche D’Anastasi helped me tremendously
with the technique of microsatellite marker development. For the marker optimisation
and downstream analysis Mrs. Georgia McDougall, and Dr. Carolyn Smith-Kuene
shared their tips and experience. I enjoyed working in the lab because of Erica, Dave,
Giana, Martin, Rose and many other lab members. They were my source of inspiration
and encouragement whenever I felt frustrated and defeated after numerous failed
attempts on many experimental trials.
The highlight of my PhD came from the intensive field work, during which I have made
great friends and received support from many volunteers, local bird watching clubs,
government agencies and private landholders. I want to express my special thanks to
Juliana Rechetelo. She was my academic “soul mate” throughout the candidature. I
could never forget the long days Juliana and I spent searching for the Black-throated
Finch and setting up mist-nets to catch them in the Australian bush. I could never forget
the sheer joy we had when the very first Black-throated Finch got caught in the net. She
has not just been a great friend, but also a great mentor in guiding me through the
process of achieving an A-Class bird banding permit in Australia. My special thanks
also go to another two dear friends as well as regular volunteers, James Tozer and
Matthew McIntosh. Whether it was labourious equipment carrying tasks, tentative bird
iii
handling jobs, or tedious data recordings, they have not complained a word. My
gratitude goes to Piet-Hein, Nadiah, Janice, Jill, Jenna, Stewart, Anne-Sophie, Petra,
Alexander, Birdlife Townsville members and many other volunteers for lending me a
hand in the field; and to Tim Nevard from Mareeba Wetlands, Tom and Sue Shephard
from the Artemis Station in Cape York and other private landholders for
accommodating me to collect samples on their properties.
The sample collection from the captive environment received immense support from
many private aviculturists across the country. For privacy reasons, I cannot mention
their names, but I would like to thank David Myers and Gary Fitt who helped me
coordinate the sample collection process in New South Wales and Queensland. Their
passion for the establishment of captive breeding stocks of the Black-throated Finch as
well as their interest for the conservation of the species have motivated me to complete
the assessment of conservation values of the captive Black-throated Finch.
My research would not have gone so smoothly without the generous guidance from the
administrative team at the then School of Earth and Environmental Science and now
College of Marine and Environmental Sciences, James Cook University. The past and
current postgraduate student officers Glen, Becky and Debbie managed to use their
magical administrative skills to help me with various paperwork so that I could spend
most of the time focusing on the research. The preparation of field equipment was also
easily managed by the experienced technical officers from the school.
I would like to make a special mention to Professor Lin Schwarzkopf. She was the
advisor of my Masters project and became the research student monitor for my PhD
candidature. I admire her knowledge and positive attitude in academic research; her
spirit and enthusiasm in education; and her compassion for students. She has offered me
iv
countless counselling sessions and given me great advice whenever I needed help both
professionally and personally. Her persuasion and encouragement towards the end of
my candidature was particularly valuable for the completion of this thesis.
My life at James Cook University in Townsville was made enjoyable in large part due
to many friends that became a part of my life. I am grateful for the time spent with
roommates and friends, for the joy and tears that we shared, for the numerous
memorable parties and road trips that we did together, and most importantly, for the
support and love that we have generously given to each other: Bingbing (Ian) Zhao,
Jonatan Marquez, Ole Henriksen, Zhangrong (Nicolas) Zhao, James Tozer, Matthew
MacIntosh, Hannah Howard, Lis Kernan, Johanna Leonhardt, Martin Aisthorpe, Ylenia
Coquille, Juan Pablo Dallos, Piet-Hein Stutterheim, Joe Partyka and Juli Gassner. I am
also grateful for the Coleman family, Nick, Gabby, Henry and Lucy, to let me stay in
their lovely house in Sydney while I wrote this thesis. Their luxurious hospitality, love
and moral support made me feel like home and most importantly, motivated me to
complete this work.
Lastly, I would like to thank my family for all their love and encouragement. For my
parents who raised me with a love of science and supported me in all my pursuits. For
my brother who took the responsibility to keep parents company while I am away from
home. And most of all for my late partner Troy Countryman who was loving,
supportive, encouraging, patient and faithful. He shared the last two years of his life
with me and I shared two most unforgettable years of this PhD with him. May he rest in
peace.
v
STATEMENT OF CONTRIBUTION OF OTHERS
Financial Support
• James Cook University (JCU) Postgraduate Research Scholarship: ~$25,800 p.a.
• Black-throated Finch Trust: $38,664
• College of Marine and Environmental Science Internal Research Funding: $9,400
• Hunter Valley Finch Club Donation: $250
Advisory Team
• Dr. James M. Moloney, College of Marine and Environmental Sciences, JCU
• Dr. Carolyn Smith-Keune, College of Marine and Environmental Sciences, JCU
• Dr. Anthony C. Grice, CSIRO Land and Water
• Dr. Britta Denise Hardesty, CSIRO Marine and Atmospheric Research
Statistical Support
• Overall support: all members of the advisory team
• Statistical design: Senior Lecturer Richard Rowe, College of Marine and
Environmental Sciences, JCU
• Spatial analysis: Nathan Ruser, School of Biological, Earth and Environmental
Sciences, University of New South Wales
• Genetic analysis:
o Dr Conrad Hoskin, College of Marine and Environmental Sciences, JCU;
o Georgia McDougall, College of Marine and Environmental Sciences, JCU
o Blanche D’anastas, College of Marine and Environmental Sciences, JCU
o Peri Bolton, Department of Biological Sciences, Macquarie University
vi
Editorial Assistance
• Overall assistance: all members of the supervisory team
• Proposal writing: Prof. Peter Valentine (retired), College of Marine and
Environmental Sciences, JCU
• Manuscript editing:
o Dr April Reside, College of Marine and Environmental Sciences, JCU;
o Dr. Mike Gardner, School of Biological Sciences, Flinders University
o Dr. Ian Watson, CSIRO Land and Water
o Henry J. Coleman, independent
Field Research Assistance
• Equipment and administrative support:
o Glen Connolly, postgraduate student officer (former), College of Marine and
Environmental Sciences, JCU
o Rebecca Steele, postgraduate student officer (former), College of Marine and
Environmental Sciences, JCU
o Debbie Berry, postgraduate student officer (current), College of Marine and
Environmental Sciences, JCU
o Robert Scott, health and safety officer, College of Marine and
Environmental Sciences, JCU
o Eric Vanderduys, CSIRO Land and Water
• Volunteers: Juliana Rechetelo, Matthew MacIntosh, James Tozer, Troy Countryman
(deceased), April Reside, Tony Grice, Eric Vanderduys, Piet-Hein Stutterheim,
Nadiah Roslan, Janice Guerrero, Jill Molan, Jenna Donaldson, Stewart McDonald,
vii
Anne-Sophie Clerc, Melissa Wood, Melissa Applegate, Petra Hanke, Ian Boyd, Ivor
Preston, Coline Gauthier and Alexander Heid
Laboratory Assistance
• Laboratory supervisor: Dr Carolyn Smith-Keune, College of Marine and
Environmental Sciences, JCU
• Technical support: Erica Todd, David Jones, Blanche D’anastas, Giana Bastos
Gomes, and Martin van der Meer
Use of Infrastructure
• Molecular Ecology and Evolutionary Laboratory (MEEL), JCU
• Wildlife Conservancy of Tropical Queensland Research Facility, Mareeba Tropical
Savanna and Wetland Reserve
• Artemis Station, Musgrave, Cape York Peninsula
• Adani Carmichael Coal Mine Camp, Galilee Basin, Central Queensland
• Avian Behavioural Ecology Lab, Macquarie University, Sydney, New South Wales
viii
ABSTRACT
More than a third of granivorous birds have declined in Australia as a result of
substantial landscape changes through grazing, altered fire regimes and land clearing for
agricultural purposes. Understanding the genetic impact of the granivore decline is
important to determine appropriate conservation management strategies because
declining populations are vulnerable to negative genetic processes such as inbreeding
depression, genetic bottlenecks and genetic drift, all of which could lead to decreased
genetic diversity and reduced evolutionary potential.
This thesis uses the threatened Black-throated Finch as a case study to investigate the
importance of applying genetic information in the conservation of declining granivorous
birds in Australia. Specifically, I developed 18 novel microsatellite markers using next
generation sequencing technology for the Black-throated Finch and tested these for
cross-species amplification success in two other co-occurring grass finch species, the
Double-barred Finch (Taeniopygia bichenovii) and the Chestnut-breasted Mannikin
(Lonchura castaneothorax) (Chapter III). Using these microsatellite markers, I then
examined the genetic diversity and population structure of four major surviving
populations of the Black-throated Finch in northern Queensland, Australia (Chapter IV).
Fine-scale spatial genetic structuring was also analysed among individuals around a
man-made dam in northern Queensland to determine the relationship between local
heterogeneous landscapes that birds occupy and the genetic distance between
individuals within tens of square kilometres (Chapter V). Last, I evaluated conservation
values of the Black-throated Finch in captivity by comparing the genetic diversity,
effective population sizes, inbreeding and relatedness between in situ and ex situ
populations (Chapter VI).
ix
The sampled four, widely-dispersed wild populations of the Black-throated Finch had
similar levels of genetic diversity. There was a significant separation between northern
and southern subspecies based on genetic structuring analysis (between subspecies FCT
= 0.034, p < 0.001). At the local scale, individual birds demonstrated strong spatial
genetic structuring separated by the Ross River Dam in the Townsville region.
Environmental Niche Modelling identified local landscape features such as vegetation
structure and the presence of water as having a strong association with the occurrence of
the Black-throated Finch. The individual genetic distance was weakly, but significantly
correlated with the geographical distance (Mantel R = 0.081, p < 0.001) and the
landscape resistance distance (Mantel R = 0.083, p < 0.001). Samples from captive birds
showed significant differentiation from wild birds. Birds in captivity had significantly
lower levels of genetic diversity (average HO = 0.35 among captive populations versus
average HO = 0.45 in the wild; average r = 2.34 in captivity versus r = 3.08 in the wild);
smaller effective population sizes; higher levels of inbreeding (F = 0.114 in the wild
versus F = 0.216 in captivity) and more admixed genetic structuring compared with
their wild counterparts.
Results of my thesis have provided evidence that the northern and southern subspecies
of the Black-throated Finch are genetically differentiated, but the differentiation is
weak. The similar level of genetic diversity across all wild populations suggested that
the genetic exchange was historically high among sampled populations despite
demonstrated low levels of demographic connectivity and recent population declines.
Fine-scale genetic analysis of birds around the Ross River Dam from the Townsville
population demonstrated that current landscape features (an open lake in particular) and
spatial separation had a combined effect on the genetic structure of the local Black-
throated Finch population. In addition, birds in captivity have lost genetic variability to
x
some degree and increased levels of inbreeding potentially may reduce the viability.
However, birds descended from extinct wild populations are still maintained in
captivity, providing potential genetic sources for future captive breeding programmes.
Conservation management strategies should therefore 1) prioritise in situ approaches,
such as frequent monitoring of population trends, identifying and maintaining existing
suitable habitats, increasing the connectivity between suitable habitats by minimising
dispersal barriers (large open waters, dense invasive plants and heavily grazed
landscapes); and 2) consider the conservation values of captive birds (particularly birds
with known origins) in establishing possible in situ breeding stocks if ex situ
conservation strategies fail to increase the population viability.
xi
TABLE OF CONTENT
ACKNOWLEDGEMENTS ........................................................................................................ I
STATEMENT OF CONTRIBUTION OF OTHERS .............................................................. V
ABSTRACT ............................................................................................................................ VIII
TABLE OF CONTENT ............................................................................................................ XI
LIST OF ABBREVIATIONS ................................................................................................ XIV
LIST OF TABLES .................................................................................................................. XVI
LIST OF FIGURES ............................................................................................................ XVIII
CHAPTER I - GENERAL INTRODUCTION ......................................................................... 1
BACKGROUND ....................................................................................................................... 2
Granivorous birds in decline ................................................................................................ 2
Genetic consequences of the decline in granivorous birds ................................................... 5
STUDY SPECIES ..................................................................................................................... 7
GENETIC APPROACHES ..................................................................................................... 10
Marker choice ..................................................................................................................... 11
Estimation of genetic diversity and genetic structure ......................................................... 14
Landscape genetics ............................................................................................................. 16
THESIS JUSTIFICATION ...................................................................................................... 18
RESEARCH AIMS ................................................................................................................. 19
THESIS OUTLINE ................................................................................................................. 19
CHAPTER II - GENERAL METHODS ................................................................................. 22
INTRODUCTION ................................................................................................................... 23
FIELD SAMPLING PROCEDURE ........................................................................................ 23
Sampling sites ...................................................................................................................... 23
Sample collection ................................................................................................................ 25
LABORATORY PROCEDURE ............................................................................................. 26
DNA extraction .................................................................................................................... 26
Microsatellite genotyping .................................................................................................... 26
Mitochondrial DNA sequencing .......................................................................................... 27
CHAPTER III - MARKER DEVELOPMENT ...................................................................... 29
ABSTRACT ............................................................................................................................ 30
INTRODUCTION ................................................................................................................... 31
METHODS .............................................................................................................................. 33
xii
RESULTS AND DISCUSSION .............................................................................................. 35
CHAPTER IV - POPULATION GENETICS ......................................................................... 39
ABSTRACT ............................................................................................................................ 40
INTRODUCTION ................................................................................................................... 41
Quantifying genetic diversity and connectivity in declining populations ........................... 41
Habitat change and the decline of granivorous birds in Australia ..................................... 42
METHODS .............................................................................................................................. 44
Field sampling and genetic procedures .............................................................................. 44
Statistical Analysis .............................................................................................................. 45
RESULTS ................................................................................................................................ 49
Marker characteristics ........................................................................................................ 49
Genetic variability ............................................................................................................... 50
Genetic structure ................................................................................................................. 52
DISCUSSION .......................................................................................................................... 55
Genetic diversity .................................................................................................................. 56
Genetic bottleneck ............................................................................................................... 57
Genetic isolation ................................................................................................................. 58
Implications for conservation management ........................................................................ 59
CHAPTER V - LANDSCAPE GENETICS ............................................................................. 61
ABSTRACT ............................................................................................................................ 62
INTRODUCTION ................................................................................................................... 63
Identifying relationships between heterogeneous landscapes and genetic structure ......... 63
Impacts of changes in the landscape on the survival of granivorous birds in Australia .... 65
METHODS .............................................................................................................................. 67
Field sampling and genetic procedures .............................................................................. 67
Statistical Analysis .............................................................................................................. 69
RESULTS ................................................................................................................................ 74
Marker characteristics ........................................................................................................ 74
Genetic structure ................................................................................................................. 75
Landscape genetics ............................................................................................................. 75
DISCUSSION .......................................................................................................................... 78
Spatial genetic structure and isolation by distance (IBD) .................................................. 79
Influences of landscape variables ....................................................................................... 81
Implications for conservation management ........................................................................ 83
CHAPTER VI - CAPTIVE POPULATIONS ......................................................................... 85
ABSTRACT ............................................................................................................................ 86
xiii
INTRODUCTION ................................................................................................................... 87
In situ and ex situ conservation ........................................................................................... 87
Conservation of the Black-throated Finch .......................................................................... 89
METHODS .............................................................................................................................. 91
Field sampling and genetic procedures .............................................................................. 91
Statistical Analysis .............................................................................................................. 92
RESULTS ................................................................................................................................ 97
Marker characteristics ........................................................................................................ 97
Genetic diversity and structure ........................................................................................... 97
Effective population size (Ne), inbreeding and relatedness .............................................. 100
DISCUSSION ........................................................................................................................ 104
Significant differentiation between captive and wild populations .................................... 104
Effective population sizes and inbreeding ......................................................................... 105
Individual assignments and relatedness ............................................................................ 107
Conservation values of captive birds and management implications ............................... 108
CHAPTER VII - GENERAL CONCLUSION ...................................................................... 111
EMPIRICAL FINDINGS ...................................................................................................... 113
Range-wide genetic structure and diversity ...................................................................... 113
Effects of landscape features ............................................................................................. 113
Conservation values of ex situ populations ....................................................................... 114
IMPLICATIONS FOR CONSERVATION MANAGEMENT ............................................. 115
RECOMMENDATIONS FOR FUTURE RESEARCH ........................................................ 117
REFERENCES ......................................................................................................................... 120
APPENDICES .......................................................................................................................... 137
xiv
LIST OF ABBREVIATIONS
(In alphabetic order)
ABBBS Australian Bird and Bat Banding Scheme
AFLP Amplified Fragment Length Polymorphism
AGRF Australian Genomic Research Facility
AMOVA Analysis of Molecular Variance
AUC Averaged Area Under the Curve
BDT BigDye® Terminator
BLAST Basic Local Alignment Search Tool
DEM Digital Elevation Model
ENM Ecological Niche Modelling
GCDI Ground Cover Disturbance Index
GIS Geographic Information System
GOFI Gouldian Finch
HWE Hardy Weinberg Equilibrium
IAM Infinite Allele Model
IBD Isolation by Distance
IUCN International Union for Conservation of Nature
LCP Least-cost Path
LD Linkage Disequilibrium
MAI Maximum Avoidance of Inbreeding
xv
MCMC Markov Chain Monte Carlo
NDII Normalised Difference Infrared Index
NDVI Normalised Difference Vegetation Index
NDWI Normalised Difference Water Index
OR Omission Rate
PCR Polymerase Chain Reaction
PIC Polymorphic Information Content
RAPD Random Amplified Polymorphic DNA
RE Regional Ecosystem
RFLP Restriction Fragment Length Polymorphism
ROC Receiver Operator Characteristic
SLATS Statewide Landcover and Tree Study (Queensland,
Australia)
SMM Single Mutation Model
SNP Single Nucleotide Polymorphism
SSR Simple Sequence Repeat
STR Short Tandem Repeat
TPM Two Phase Model
TWI Topographic Wetness Index
USGS United States Geological Survey
xvi
LIST OF TABLES
Table II-1 Sampling sizes of the Black-throated Finch (Poephila cincta) from wild and captive
populations in Australia ...................................................................................................... 25
Table III-1 Details for 18 Black-throated Finch (Poephila cincta cincta) microsatellite loci
developed from 454 shotgun sequences .............................................................................. 37
Table III-2 Cross-species amplification success (number of scorable individuals out of total
individuals amplified), allele size range in base pairs (bp) and number of alleles (NA) for
the novel Black-throated Finch (Poephila cincta cincta) microsatellite loci in the Double-
barred Finch (Taeniopygia bichenovii) and Chestnut-breasted Mannikin (Lonchura
castaneothorax) ................................................................................................................... 38
Table IV-1 Measurements of genetic diversity for each of the Black-throated Finch (Poephila
cincta) populations. NPA: number of private alleles; HO: observed heterozygosity; HE:
expected heterozygosity; r: average allelic richness; GD: genetic diversity index; FIS:
inbreeding coefficient .......................................................................................................... 51
Table IV-2 Counts of six identified mitochondrial haplotype types in each population of the
Black-throated Finch (Poephila cincta). Relative frequencies within each population
shown in brackets ................................................................................................................ 51
Table IV-3 Result comparison of the two ad-hoc bottleneck analysis in each of the Black-
throated Finch (Poephila cincta) population. M: ratio of total number of alleles to the
range of allele sizes; MC: critical value of M; PW: probability value of one-tailed Wilcoxon
sign rank test for heterozygosity excess. For M ratio method, if M < MC; and for
Heterozygosity excess method, if PW < 0.05, a recent bottleneck is likely to be present ... 52
Table IV-4 Analysis of molecular variance (AMOVA) grouping populations of the Black-
throated Finch according to subspecies (Poephila cincta cincta and P. c. atropygialis). ... 53
Table V-1 Sampling efforts of the Black-throated Finch (Poephila cincta cincta) in Townsville,
Queensland Australia. ......................................................................................................... 67
Table V-2 The relative contributions and jackknife test results of environmental variables to the
MAXENT model. RE: regional ecosystems; GCDI: ground cover disturbance index; TWI:
topographic wetness index; NDWI: normalised difference water index; NDVI: normalised
difference vegetation index; NDII: normalised difference infrared index .......................... 76
Table V-3 The Euclidean distance (in km, above the diagonal) and the least cost path (LCP)
distance (below the diagonal) between each pair of sampling locations ............................ 77
xvii
Table VI-1 Measurements of genetic diversity and results of genetic bottleneck analysis for
each of the Black-throated Finch (Poephila cincta) populations. NPA: number of private
alleles; HO: observed heterozygosity; HE: expected heterozygosity; r: average allelic
richness. M: ratio of total number of alleles to the range of allele sizes; MC: critical value
of M; PW: probability value of one-tailed Wilcoxon sign rank test for heterozygosity
excess. For M-ratio method, if M < MC; and for Heterozygosity excess method, if PW <
0.05, a recent bottleneck is likely to be present .................................................................. 98
Table VI-2 Analysis of molecular variance (AMOVA) grouping populations of the Black-
throated Finch according to sources (wild and captive) ...................................................... 99
Table VI-3 Comparison of estimates of effective population sizes (Ne) in each population using
three different methods. Θ: mutation-scaled effective population size; *: captive
populations ........................................................................................................................ 101
Table VI-4 Pairwise comparisons of the inbreeding coefficient (F) between and within each
Black-throated Finch population. Numbers along the diagonal are F values within each
population and variances are summarised in brackets. Numbers below the diagonal are F
values between each population pair. * indicates significant difference with 95%
confidence interval and ** indicates significant difference with 99% confidence interval
........................................................................................................................................... 102
xviii
LIST OF FIGURES
Figure I-1 Percentage contributions of different bird groups to the threatened avifauna of
Australia. Data are from the Action Plan for Australian Birds (Garnett and Crowley 2000a)
............................................................................................................................................... 3
Figure I-2 The Black-throated Finch (Poephila cincta), northern subspecies (P. c. atropygialis)
with a black rump (left) and southern subspecies (P. c. cincta) with a white rump (right).
Photos by L.S. Tang .............................................................................................................. 8
Figure I-3 Distribution maps of the Black-throated Finch (Poephila cincta) (from the Black-
throated Finch Recovery Team). The dotted line indicates an estimated boundary between
northern (P. c. atropygialis) and southern subspecies (P. c. cincta). Each dot indicates a
recognised and recorded sighting. (a) all historical recorded sightings and (b) sightings
recorded after 2000. .............................................................................................................. 9
Figure II-1 Sampling locations of the wild Black-throated Finch (Poephila cincta) in
Queensland, Australia. ........................................................................................................ 24
Figure IV-1 Log posterior probabilities (ln P(D), left) and Δk (right) of the microsatellite data
for each number of genetic clusters (k) tested ..................................................................... 54
Figure IV-2 Individual membership coefficients derived from Bayesian inference of genetic
structure across four Black-throated Finch populations using admixture model with
(bottom, k = 2) a priori populations. A single vertical line represents an individual ......... 54
Figure IV-3 Mitochondrial DNA haplotype network of the selected Black-throated Finch
samples from each population. Circle size indicates proportion of individuals assigned that
haplotype. Solid lines connecting haplotypes are proportional to the number of mutations
separating them. The network is rooted to the Gouldian Finch (GOFI) ............................. 55
Figure V-1 Sampling locations (black dots) of the Black-throated Finch (Poephila cincta
cincta) around the Ross River Dam in the Townsville region, Queensland ....................... 68
Figure V-2 Individual membership coefficients derived from Bayesian inference of the genetic
structure of the Black-throated Finch population in the Townsville region, using admixture
model (inferred genetic cluster k = 3) with sampling locations as prior. Each cluster is
represented as a different colour and a single vertical line represents an individual .......... 75
Figure V-3 Habitat suitability of the Black-throated Finch around the Ross River Dam in
Townsville, Queensland, Australia. Clear white circles represent sighting records between
2009 and 2014. White dots with black outlines show sampling locations of this study ..... 77
xix
Figure V-4 Identified least-cost pathways (yellow lines) from PATHMATRIX simulations,
representing potential dispersal corridors of the Black-throated Finch among sampling
locations (white dots) in the Townsville region. Thickness indicates the cost values – the
thicker the line, the lower the cost of dispersal ................................................................... 78
Figure VI-1 The genetic distance in relation to allele richness among captive (grey triangles)
and wild (black squares) populations ................................................................................ 100
Figure VI-3 Comparison of the individual relatedness among wild (CEQ, CYP, FNQ and TSV)
and captive (CAP) populations. Each bar represents the percentage of dyads in the relevant
population .......................................................................................................................... 102
Figure VI-2 Individual membership coefficients derived from Bayesian inference of genetic
structure across all captive and wild Black-throated Finch populations using admixture
model (top, k = 2); and among all captive populations using admixture model (bottom, k =
8). A single vertical line represents an individual. Each colour represents a genetic cluster
........................................................................................................................................... 103
2
BACKGROUND
Granivorous birds in decline
Granivorous (seed-eating) birds are a major component of the avifauna in most
terrestrial communities. In Australia, 20% of the land bird species are granivores
(Franklin et al. 2000). These birds have evolved independently within various taxa
(finches, parrots, quail-like birds, pigeons and doves), and they are characterised by an
array of specific morphological and ecological features, such as a beak that is suitable
for extracting seeds, an ability to form large feeding aggregations, and a requirement for
frequent water consumption and seed resources (Wiens and Johnson 1977). The specific
resource requirements have made granivorous birds particularly vulnerable to changes
in the landscapes they occupy. Approximately one-third of Australian granivorous bird
species have declined and they represent more than a quarter of all threatened land bird
taxa in Australia (Figure I-1) (Garnett and Crowley 2000a).
The decline of granivorous birds has occurred unevenly across the country. The decline
is greatest in northern Australia, where the tropical and subtropical savannahs hold the
largest granivorous bird assemblages (State of the Environment Advisory Council 1996,
Franklin et al. 2005). Declines have also occurred in the arid zone (Reid and Fleming
1992). For example, six finch species are under threat and they all occur predominantly
in northern Australia. These finches include the Gouldian Finch (Erythrura gouldiae,
Endangered), the Pictorella Mannikin (Heteromunia pectoralis, Near Threatened), the
White-bellied Crimson Finch (Neochmia phaeton evangelinae, Endangered), the Star
Finch (Neochmia ruficauda clarescens, Endangered; N. r. ruficauda, Extinct; N. r.
subclarescens, Near Threatened), the Southern Black-throated Finch (Poephila cincta
cincta, Vulnerable) and the Diamond Firetail (Stagonopleura guttata, Near Threatened).
3
Six parrots, four quail-like birds, and five pigeons and doves from northern Australia
are also under threat (Garnett and Crowley 2000a).
There have been more substantial landscape changes in southern Australia than in the
north since European settlement. Nonetheless, more recent changes in northern
Australia have resulted in the range decline of granivore species (Woinarski 1999). The
decline is likely to be more severe in areas where grazing intensity is higher and
pastoralism has been established longer in northern Australia (Whitehead 2000, Sharp
and Whittaker 2003). In particular, extensive grazing alters grass species composition,
productivity, seed production, and therefore overall food resources for granivores (e.g.
Walker et al. 1997). Grazing in combination with prolonged drought is also likely to be
a major factor in the decline of the Star Finch (N. r. ruficauda). This is because the Star
Finch usually occurs in long grass close to water and, during droughts, the continuous
presence of livestock around these water bodies severely alters this critical habitat
(Holmes 1998).
Figure I-1 Percentage contributions of different bird groups to the threatened avifauna of Australia. Data are from the Action Plan for Australian Birds (Garnett and Crowley 2000a)
4
The decline may be related to the changes in frequency and timing of fire since
European settlement (Walker et al. 1997, Russell-Smith et al. 2003). The general trend
is of decreased frequency on pastoral properties and increased frequency in high-
rainfall, non-pastoral areas (Williams et al. 2002, Andersen et al. 2005). These changes
have important implications for biodiversity. The experimental fire regimes at Kakadu
National Park have had a combination of positive and negative impacts on only five out
of 25 bird species. Some birds including the Brown Goshawk (Accipiter fasciatus) and
the Red-backed Kingfisher (Todiramphus pyrrhopygia) showed positive responses
(Andersen et al. 2005). However, the impact of changes in fire regimes on granivorous
birds is generally negative. The habitat of the Golden-shouldered Parrot (Psephotus
chrysopterygius) has also been altered due to changes in the fire regime creating a
coarser fire mosaic limiting the dispersal of the bird during the wet season (Garnett and
Crowley 2002). Increased frequency or extent of fires also results in increased mortality
of eggs and hatchlings of the Partridge Pigeon (Geophaps smithii) in the dry season as
the bird nests on the ground within clumps of grass (Woinarski 2004).
The loss of granivorous birds is also related to changes in habitat arrangement and
vegetation structure of the landscape. Heterogeneous landscapes, with their greater
diversity of habitats, provide opportunities for granivores under different environmental
conditions (Woinarski 1999, Franklin et al. 2005). Grazing, invasive species and fire all
change the landscape, particularly the understorey vegetation. Granivorous birds in
savannahs are susceptible to such environmental variation because of their specialised
diet (Franklin et al. 2005). For example, the Gouldian Finch has a restricted diet in the
wet season with its food limited to cockatoo grass (Alloteropsis semialata) and golden
beard grass (Chrysopogon fallax). Both of these grasses are selectively grazed by cattle
and horses leading to a reduction of critical wet season seed resources for the bird
5
(O'Malley 2006). Invasive/exotic plant species are also involved in landscape changes.
The rapid spread of exotic pasture grasses and woody weeds into savannahs can replace
native species reducing available food resources for granivores, and increasing the fuel
load leading to increased fire intensities (Rossiter et al. 2003). The introduced Rubber
Vine (Cryptostegia grandifolia), for example, has invaded and replaced the native
vegetation in some areas of the habitat of the White-bellied Crimson Finch (Neochmia
phaeton evangelinae) possibly contributing to the decline of this bird (Garnett and
Crowley 2000a).
Genetic consequences of the decline in granivorous birds
The decline of granivorous birds may have negative genetic consequences, such as
inbreeding depression, loss of neutral genetic variation, fixation of deleterious alleles
and genetic bottlenecks, which are usually associated with small or fragmented
populations (Herdrick 2011). Population fragmentation and isolation may have
detrimental effects on the fitness and viability of surviving populations (Ferrière et al.
2004). Failure to consider genetic consequences of the decline of a species may result in
underestimation of the probability of extinction and the implementation of inappropriate
conservation strategies (Frankham and Ralls 1998, Frankham 2005). For example, a
molecular study of the Gouldian Finch identified recent population declines followed by
episodes of population expansion throughout northern Australia. Although no genetic
evidence of inbreeding or recent bottlenecks was found in wild populations of the
Gouldian Finch, heterozygosity was significantly lower. There was also evidence for a
recent population bottleneck in a captive stock which was part of a reintroduction
programme for the maintenance and persistence of the species (Esparza Salas 2008).
6
Introducing additional individuals from diverse wild populations into this captive
population may increase the genetic variability of released birds (Esparza Salas 2008).
Genetic data can also identify distinct populations, guiding management actions. For
example, using microsatellite DNA markers, the Yellow Rail (Coturnicops
noveboracencis) of Oregon, USA was identified as a genetically distant population with
the lowest genetic diversity comparing with other major populations of the species
(Miller et al. 2012). The Black Grouse (Tetrao tetrix) in Great Britain, on the other
hand, has become fragmented and isolated into three genetically distinct populations
with little migration between them (Höglund et al. 2011). The genetic differentiation
observed is likely due to genetic drift in small and fragmented sub-populations and,
because these events are recent, there may be little advantage to managing these
populations separately (Höglund et al. 2011).
Landscape features and heterogeneity are often reflected in population genetic structure
within and among species (Miller and Haig 2010, Safner et al. 2011). Changes in
landscapes are often associated with habitat loss, which contributes to population
decline possibly resulting in reduced genetic diversity of remaining populations
(Templeton et al. 1990, Keyghobadi 2007). Habitat loss and fragmentation due to
landscape changes lead to population bottlenecks and local extinction, which in turn
result in the loss of overall genetic diversity (Keyghobadi 2007). Measures of genetic
differentiation, for example, are negatively associated with percentage of forest cover
between breeding sites of the Golden-cheeked Warbler (Dendroica chrysoparia) in
Texas, USA, and positively associated with percentage of agricultural land between
sites (Lindsay et al. 2008). Although the Golden-cheeked Warbler is a migratory
species, the vast majority of individuals nest within 4km of where they were born. The
7
observed high level of genetic differentiation among populations was due to the
agriculture related fragmentation of its forested breeding habitat (Lindsay et al. 2008).
Other landscape features are also associated with the genetic structure of birds. For
example, water barriers reduce gene flow between populations of the Song Sparrow
(Melospiza melodia) and generate geographic variations in the genetic structure (Wilson
et al. 2011).
STUDY SPECIES
The Black-throated Finch is endemic to Australia and was once distributed from north-
eastern New South Wales to Cape York Peninsula in grassy woodlands dominated by
eucalypts, melaleucas or acacias (Baldwin 1976). It typically occurs near water sources,
where it feeds on grass seeds and may also hawk after flying insects (Zann 1976)
(Rechetelo unpublished data and pers. comm.). There are two subspecies of P. cincta
(Figure I-2). The northern (black-rumped) form P. c. atropygialis occurs north from
Mareeba on the Atherton Tablelands, Queensland and across Cape York Peninsula, and
its populations are widespread and secure. The southern (white-rumped) form P. c.
cincta once ranged from the Atherton Tablelands to as far south as north-east New
South Wales, but it has disappeared from much of the southern extent of its range
(Schodde and Mason 1999) (Figure I-3).
Since 1998, the southern form has been recorded in only four bioregions from central to
north Queensland and some evidence suggests that the species may have declined by
50% in the past 10 years (Environment Australia 2000, Black-throated Finch Recovery
Team 2007). Currently, this subspecies is listed as “Endangered” under the national
legislation (Environment Protection and Biodiversity Conservation Act 1999) and under
8
the Queensland state legislation (Nature Conservation Act 1992). The Black-throated
Finch is listed as “Presumed Extinct” in New South Wales under the state legislation
(Threatened Species Conservation Act 1995). Under international conventions, at the
species level, it is listed as “Near Threatened” in the IUCN Red List of Threatened
Species 2011 and under Appendix II of the CITES 2011.
The decline of the Black-throated Finch began early in the 1900s with the expansion of
pastoralism into its habitat (Franklin 1999). Recent sighting records indicate that the
distribution of P.c. cincta is severely fragmented. To date, there has been no reliable
information or accurate estimate of the occurrence, size, number or the natural
fluctuations of the populations and subpopulations of the species. However, the decline
of P. c. cincta is continuing according to the on-going monitoring activities conducted
by the Black-throated Finch Recovery Team (Black-throated Finch Recovery Team
2007). Possible threats to the bird include: (1) clearance and fragmentation of
woodlands, riparian habitats and Acacia scrublands; (2) degradation of habitat by
domestic livestock and rabbits, including the alteration of vegetation structure, the
availability of food during the wet season and the reduction of fuel loads, (3) invasion
Figure I-2 The Black-throated Finch (Poephila cincta), northern subspecies (P. c. atropygialis) with a black rump (left) and southern subspecies (P. c. cincta) with a white rump (right). Photos by L.S. Tang
9
of habitat by exotic plants, including grasses; (4) trapping for aviculture; and (5)
predation by introduced animals, such as feral cats (Natural Resource Assessment
Environmental Consultants 2005, Black-throated Finch Recovery Team 2007).
The movement patterns of the Black-throated Finch are poorly known. The southern
subspecies was considered as resident in its known locations including northern New
South Wales, northern and central Queensland in Australia (Longmore 1978, Morris et
al. 1981, Britton and Britton 2000, Forshaw et al. 2012). Recent sighting records and
research has shown that the Black-throated Finch in the Townsville region can move up
to 20km (J. Rechetelo, unpublished data). Some movements may be undertaken due to
Figure I-3 Distribution maps of the Black-throated Finch (Poephila cincta) (from the Black-throated Finch Recovery Team). The dotted line indicates an estimated boundary between northern (P. c. atropygialis) and southern subspecies (P. c. cincta). Each dot indicates a recognised and recorded sighting. (a) all historical recorded sightings and (b) sightings recorded after 2000.
10
changes in food availability as a result of increased rainfall or drought (Baldwin 1976,
Mitchell 1996). Post-breeding dispersal appears to be limited as birds are still
encountered at their breed sites during non-breeding seasons (Mitchell 1996, Natural
Resource Assessment Environmental Consultants 2007).
Given the limited movement of the Black-throated Finch (particularly the southern
subspecies) and its fragmented populations, it is likely the risk of extinction of the bird
is high. In addition, rapid changes in the heterogeneous landscape that the bird uses
could mean that sub-populations become fragmented leading to an increased probability
of deleterious genetic consequences.
GENETIC APPROACHES
Using molecular data to examine genetic variation in order to understand the ecological
and evolutionary processes within and among population and/or species has become a
great complementary tool to traditional methods using phenotypic (e.g. morphology,
physiology and behaviour) and ecological data (Avise 2004, Sunnucks and Taylor
2008). Molecular information allows us to directly quantify genetic diversity, measure
population admixture, estimate inbreeding, trace historical patterns of dispersal and to
identify taxonomic lineages (Avise 2004). With the rapid advancement of molecular
technology and development of more robust molecular markers, an increasing variety of
approaches have become available, providing an array of powerful tools in the field of
ecological research.
11
Marker choice
Molecular markers allow the relatively rapid detection and characterisation of genetic
variation. However, no single molecular marker is ideally suited to all evolutionary and
ecological studies. Therefore, the choice of molecular markers is crucial in order to
address particular research questions. For example, some markers such as microsatellite
DNA and DNA sequences that host single nucleotide polymorphisms (SNPs) exhibit
higher mutation rates and evolve fast enough to infer recent evolutionary histories.
Other markers, such as the genomic DNA sequence, provide better coverage of the
entire genome. Mitochondrial DNA, on the other hand, is only maternally inherited, and
is thus useful for detecting female biased genetic structure (Balkenhol et al. 2009, Wang
2011). For population and individual level studies, the molecular markers need to be
fast evolving and highly variable (Herdrick 2011). The following molecular markers are
commonly used in population studies.
Microsatellite DNA/Single Sequence Repeats (SSRs)
Microsatellite loci have been the most widely used molecular markers in conservation
genetic studies. They are DNA sequences composed of short tandem repeats (STRs) or
simple sequence repeats (SSRs) of two to six nucleotides. For example, (AC)10 is a
sequence of 10 repeated units of the dinucleotides AC, i.e.
ACACACACACACACACACAC. These loci are generally codominant, selectively
neutral and multi allelic. The number of repeats is highly heterogeneous. Unlike
mitochondrial DNA, microsatellites often present high levels of inter- and intra-specific
polymorphism, which can be easily determined by electrophoresis or automated size
separation of the polymerase chain reaction (PCR) amplified DNA fragments.
Therefore, microsatellite markers are widely used in gene mapping and population
12
genetic studies (review in Goldstein and Pollock 1997, McDonald and Potts 1997).
They can also be used to evaluate allele relatedness (Goldstein et al. 1995) .
Microsatellite loci are the first choice in most conservation genetic studies, but they
have some drawbacks. Primers amplifying microsatellites have to be specifically
designed for each species. The transferability of these primers decreases significantly as
species become less closely related (Jarne and Lagoda 1996). Therefore, the
development of microsatellite primers is potentially time consuming and expensive.
Moreover, in the genome of some species, microsatellites can be sparse and so difficult
to find (Zane et al. 2002, Schlötterer 2004). As a result, the use of microsatellites in less
studied species with little genetic information (non-model species) is limited because of
insufficient marker resources (Luikart et al. 2003). However, with improvements in
sequencing technologies and the greater availability of sequence databases, the
discovery and typing of microsatellites in non-model species has become more
accessible (Hudson 2008).
Single Nucleotide Polymorphisms (SNPs)
SNPs refer to the bi-allelic variations of single nucleotides within almost identical DNA
sequences from individuals of the same species. Heterozygotes are identified by
different nucleotides at specific sites whereas homozygotes show the same nucleotide.
SNPs are by far the most abundant and accurate type of polymorphism in genomes
(Schlötterer 2004). Consequently, these markers have become increasingly important in
genotyping (especially large genome scanning) and biomedical research (Williams et al.
2010). They are useful markers for studies of disease development (e.g. Johnson et al.
2007, Amos et al. 2008) and genetic adaptation (e.g. Mauricio et al. 2003, Namroud et
al. 2008). SNPs have also been used more frequently than other markers in landscape
13
genetics to quantify genetic differences between individuals, populations and species
(Manel et al. 2010). In vertebrates, for example, one SNP occurs on average every 100
to 1000 base pairs and is usually in linkage disequilibrium with other SNPs (Vignal et
al. 2002). Unfortunately, detecting SNPs is a costly time-consuming task and generally
requires a priori information on the studied genome (Morin et al. 2004), with the
exception of newly developed technologies such as the restriction site associated DNA
sequencing (RAD-seq) and genotyping by sequencing (GBS), which can be used in
non-model species (Jiang et al. 2016). Also, the marker resources are not readily
available for most non-model species.
Mitochondrial DNA & Restriction Fragment Length Polymorphisms (RFLPs)
Mitochondrial DNA play important roles as genetic markers in population and
conservation biology. Their popularity comes from their uniparental inheritance
(usually maternal), lack of recombination and simple composition of sequences
(Harrison 1989). As a result, these markers have been widely used in phylogenic and
phylogeographic research to trace maternal genealogies and detect dispersal events
(Herdrick 2011). Mitochondrial DNA also have relatively fast rates of divergence
(especially some non-coding regions) that, in principle, makes them useful for detecting
variations among individuals within a landscape at temporal scales (Vandergast et al.
2007, Swart et al. 2009, Measey and Tolley 2011). However, these markers do not
always provide enough variation among individuals at fine spatial scales, which has
limited their applications in landscape genetics (Holderegger and Wagner 2008,
Bohonak and Vandergast 2011).
Variation of these markers is usually assessed by DNA sequencing or by restriction
enzymes (e.g. EcoR1). DNA sequencing is fast and accurate and it has been widely used
14
for mitochondrial DNA based population genetic analyses. Restriction enzymes
recognise specific sites in mitochondrial DNA. The spacing between each two sites may
vary in length among individuals or populations resulting in fragments of different
lengths (restriction fragment length polymorphisms, RFLPs) after DNA digestion.
However, this technique requires large amounts of DNA and produce only limited
information, so its application has been limited to some microorganisms including
pathgens (Brudey et al. 2006) and fungi (e.g. Kinloch et al. 1998).
Estimation of genetic diversity and genetic structure
Genetic diversity, estimated using either allele or genotype frequencies, is important in
population studies as it determines the ability of any population to adapt and evolve to
changing conditions with implications for their long-term survival. Descriptive
measures, including allelic richness, allelic diversity and observed heterozygosity, can
help characterise genetic diversity of each population (Beebee and Rowe 2008, Freeland
et al. 2011). For example, a deficiency in observed heterozygosity could indicate that
the population is not in Hardy-Weinberg Equilibrium and may be undergoing certain
genetic processes, such as inbreeding or genetic drift.
Another important feature of population genetic studies is the determination of genetic
structure and the measurement of levels of gene flow between populations. Gene flow
affects the ecology and evolution of natural populations (Hanski and Gilpin 1997). For
example, a high level of gene flow reduces divergence and so can decrease the
likelihood of reproductive isolation. Frequent gene flow among populations can
enhance local adaptation to heterogeneous landscapes (Lenormand 2002). Furthermore,
gene flow also affects important ecological properties such as species extinction rates
(Epps et al. 2005), evolution of species distributions (Alleaume-Benharira et al. 2006),
15
population persistence (Palstra and Ruzzante 2008) and the synchrony of population
size changes (Richards et al. 1999).
Many statistical approaches have been proposed to evaluate gene flow. Most
commonly, gene flow is referred to as Nem, the product of individual effective
population size Ne and migration rate m, and is estimated from frequencies of genetic
markers by assuming equilibrium between gene flow and neutral genetic drift (Mallet
2001). Under the “island model”, where migrants can come from anywhere in the
metapopulation (therefore the gene frequency is constant), gene flow can be estimated
by calculating the fixation index 𝐹ST =1
1+𝑁𝑒𝑚 (Wright 1969). According to this
equation, when populations are small and/or migrations between populations become so
rare that 𝑁𝑒𝑚 ≪ 1 drift outweighs gene flow and 𝐹ST → 1. Conversely, if populations
are large and/or migrations are frequent enough so that 𝑁𝑒𝑚 ≫ 1, gene flow dominates
drift and 𝐹ST → 0. In other words, the greater the FST is, the greater the genetic
differentiation between populations becomes. This relationship can broadly apply in
more realistic population structures, such as the “stepping stone” model, where migrants
are exchanged mainly between local subpopulations (Slatkin and Barton 1989).
FST is suitable for datasets consisting of unlinked neutral markers, such as single
nucleotide polymorphisms (SNPs) with two alleles per locus. FST provides a basis for
measuring genetic distance when population divergence is caused by genetic drift
(Reynolds et al. 1983). Depending on the properties of selected markers, there are a
number of other related statistics developed as analogues of FST. For example, the
microsatellite markers assume the stepwise mutation model (SMM), where novel alleles
are created by adding or deleting repeated units of microsatellite sequences. Therefore,
RST, an analogue of FST incorporating SMM, is thought to reflect more accurately in the
16
evolution of microsatellite markers (Slatkin 1995, Balloux and Lugon-Moulin 2002).
Other statistics, such as the estimate of divergence Dest, derived from the true allelic
diversity, is better for markers with more than two alleles, because it avoids impacts of
within-population diversity when estimating genetic differentiation among populations
(Jost 2008).
Landscape genetics
Landscape genetics was first defined as an “amalgamation of molecular population
genetics and landscape ecology” (Manel et al. 2003). This field combines advanced
molecular tools with landscape ecology and spatial statistics to investigate and quantify
the effects of landscape variables (e.g. barriers, composition, configuration and habitat
quality) on micro-evolutionary processes, i.e. gene flow and selection (Holderegger and
Wagner 2008, Jaquiéry et al. 2011). It usually requires two key steps (Manel et al.
2003): 1) population genetics to detect discontinuities between populations or
individuals; and 2) spatial correlation to identify effects of landscape features. Results
provide information for further investigation in other disciplines including ecology,
evolution, conservation, wildlife management and epidemiology (Zannèse et al. 2006,
Neel 2008, Root et al. 2009, Wang et al. 2009, Goldberg and Waits 2010).
The rapid development of molecular genetics and technologies in spatial analysis has
led to a rapid increase in landscape genetic studies in recent years. Up to 2008, there
were over 600 articles dealing with landscape genetics published in various journals
(Storfer et al. 2010). These articles have addressed a wide variety of research questions
on a large range of taxa including vertebrates, invertebrates, plants, bacteria, viruses,
lichens and fungi (Storfer et al. 2010). Five major research categories were identified
through these publications (Storfer et al. 2007): (1) quantifying the influence of
17
landscape variables and configuration on genetic variation; (2) identifying barriers to
gene flow; (3) identifying source-sink dynamics and movement corridors; (4)
understanding the spatial and temporal scales of an ecological process; and (5) testing
species-specific ecological hypotheses.
Compared with traditional population genetics, landscape genetics explicitly tests the
effects of landscape heterogeneity on genetic structuring within and among populations
(Holderegger and Wagner 2008, Storfer et al. 2010). Existing population genetic models
assume large population sizes with high degrees of spatial symmetry (Segelbacher et al.
2010). Simple isolation-by-distance (IBD) models including only geographical
distances between occupied habitat fragments have been demonstrated to be inadequate
for explaining variations in genetic distance (e.g. Spear et al. 2005, McRae 2006, Spear
and Storfer 2008, Murphy et al. 2010). Movements of animals may not only be limited
by the spatial separation of habitat patches, but also by other landscape features, for
example, potential barriers (rivers, high mountains, anthropogenic constructions, etc.),
habitat transformation (agriculture, timber forests, etc.), or climatic characteristics
(temperature, precipitation, humidity, etc.). Thus, multivariate models that take into
account arrays of landscape characteristics to analyse the cause of population genetic
structuring are becoming increasingly important to population genetic studies
(Holderegger and Wagner 2008, Balkenhol et al. 2009). There have been no studies
conducted to examine the association between landscape features and population
genetic structures of the Australian granivores up to date.
18
THESIS JUSTIFICATION
Northern Australia has undergone substantial landscape changes through grazing,
altered fire regimes and land clearing for agricultural purposes. Such changes may have
contributed to the decline of approximately one-third of granivorous bird species of the
region (Garnett and Crowley 2002). Many species of granivorous birds in northern
Australia are sedentary (Higgins et al. 2006). Although the relationship between
mobility and genetic structure has not been quantified, the population structure and
genetic diversity of these species is more likely to be affected by landscape changes
than that of other migratory species. This is because species that are highly dispersive
are more likely to find and exploit new patches of resources when local environments
become less favourable (Thomas 2000, Bergerot et al. 2012).
This research investigates the genetic diversity and structure of one granivorous bird
species, the endangered Black-throated Finch, in response to landscape structure over
multiple scales in northern Queensland, Australia. Applying molecular tools, my project
is the first study to examine the population genetic structuring of a granivorous bird in
relation to landscape features in Australia. It has improved the knowledge of the
endangered Black-throated Finch in several critical areas, including: population genetic
diversity, phylogenetic relationships between subspecies and conservation values of
captive populations. Such genetic information is important to the understanding of the
ecological impacts of landscape changes and to to the identification of genetically
distinct populations relevant to conservation. It has also provided a means to recognise
suitable habitats for the maintenance and persistence of the species.
19
RESEARCH AIMS
My thesis uses population and landscape genetics as well as phylogenetic approaches to
answer a series of questions at local to regional scales, and at population, subspecies
and species levels. The overall aims of my project are to:
1. Develop species-specific molecular tools to study the population genetics of the
Black-throated Finch.
2. Examine the genetic structure and diversity of the Black-throated Finch at
several locations across its range in northern Queensland, Australia.
3. Examine the relationship between current landscape features and the genetic
structure of the Black-throated Finch at a fine spatial scale (in proximity of a
man-made dam in northeastern Queensland).
4. Examine and compare genetic structure and diversity of wild and captive
populations of the Black-throated Finch to determine the conservation values of
the captive birds.
THESIS OUTLINE
In Chapter II, I describe field sampling protocols and laboratory procedures that were
used to generate genetic data for the analysis in subsequent chapters. The number of
samples and type of molecular markers used in each analysis are also explained in this
chapter.
In Chapter III, I illustrate the development of novel microsatellite markers used for the
Black-throated Finch using next-generation (454 shotgun) sequencing technology.
Cross-species amplification was also performed on two related and co-occurring
20
species, the Double-barred Finch (Taeniopygia bichenovii) and the Chestnut-breasted
Mannikin (Lonchura castaneothorax) to determine the value of these markers in other
closely related species.
In Chapter IV, I evaluate the genetic diversity and structure of four populations of the
Black-throated Finch. These four populations represent the majority of the birds known
to be surviving across its range. Specifically, I estimated the richness and
heterozogosity of both mitochondrial and microsatellite markers; examined the level of
inbreeding; and evaluated the possibility of genetic bottleneck within each population.
Bayesian clustering analysis was used to determine broad scale structuring among
populations of the Black-throated Finch.
In Chapter V, I use a landscape genetic approach to examine the fine scale genetic
structuring within the Townsville population of the Black-throated Finch in relation to
various landscape variables. Environmental niche modelling was used to generate a
landscape resistance matrix, which was then used to correlate with a genetic distance
matrix.
In Chapter VI, I evaluate the conservation value of captive populations of the Black-
throated Finch. Specifically, I sampled birds from various aviculturists and compared
them with wild populations with respect to genetic diversity, population structuring,
individual inbreeding, relatedness and presence of genetic bottleneck. I also performed
individual assignments to determine possible origins of the captive birds with unknown
ancestry information.
In Chapter VII, I synthesise overall findings from each chapter and highlight the key
results that are important to the conservation of the Black-throated Finch. I also discuss
implications for future studies and limitations of this research. Finally, I make detailed
21
recommendations for the conservation management of the Black-throated Finch based
on the genetic knowledge developed in this research.
23
INTRODUCTION
I collected samples for the analysis in each chapter in the same manner and followed the
same laboratory procedures to extract DNA, to run polymerase chain reactions (PCRs)
and to genotype all individuals. In this chapter, I detail the common procedures I have
used both in the field and in the laboratory.
FIELD SAMPLING PROCEDURE
Sampling sites
On the basis of recent sightings records, I selected sites across the current range where
the Black-throated Finch (Poephila cincta) had been seen regularly since 2009. The
southern form (P. c. cincta) was sampled in the Townsville region (19.4333°S,
146.7667°E) in northern Queensland as well as in the Desert Uplands (22.1167°S,
146.4500°E) in central Queensland. The northern form (P. c. atropygialis) was sampled
at Mareeba (16.9167°S, 145.3500°E) in far north Queensland. Samples for Lakefield
National Park (14.8184°S, 144.2113°E) on Cape York Peninsula were obtained from a
previous study (Maute 2011). I have also obtained additional samples of the same study
by K. Maute from the Townsville region. All samples from the wild were collected
between 2009 and 2013 (Figure II-1 and Table II-1).
I sourced samples of captive birds from aviculturists who keep records of each
individual bird they possess through the Queensland Finch Society, the Finch Society of
Australia and the Hunter Valley Finch Club in the states of Queensland and New South
Wales in Australia (Appendix F). Each finch owner collected blood samples of their
own birds following the same FTA© card protocol as mentioned below.
24
In Chapter III, only a subsample of the Townsville population was used for
microsatellite marker development. In Chapter IV, all wild samples were used to
examine the range-wide genetic diversity and structure. In Chapter V, the fine-scale
landscape genetic analysis used only individuals sampled in the Townsville region and
in Chapter VI, all wild and captive birds were used for the comparative analysis to infer
conservation values of the ex situ populations (numbers of individuals used for each
analysis are specified in relevant chapters).
Figure II-1 Sampling locations of the wild Black-throated Finch (Poephila cincta) in Queensland, Australia.
25
Table II-1 Sampling sizes of the Black-throated Finch (Poephila cincta) from wild and captive populations in Australia
Region (wild)/Origin (captive) Population code
Source Sample size
Townsville Coastal Plain, North Queensland
TSV Wild 86 + 48*
Desert Uplands, Central Queensland CEQ Wild 44 Mareeba Wetlands, Far North Queensland FNQ Wild 16 Lakefield National Park, Cape York Peninsula
CYP Wild 48*
Subtotal 242 Armidale, North New South Wales NSW Captive 2 Murgon, Southeast Queensland SEQ Captive 22 Rockhampton, Central Queensland ROC Captive 18 Ayr, North Queensland AYR Captive 6 Unknown origin UNK Captive 49 Subtotal 97 TOTAL 339 * Samples collected by Kim Maute in 2009, 48 from TSV and 48 from CYP
Sample collection
The scarcity and patchy distribution of the Black-throated Finch makes it difficult to
sample outside of a few known areas. However, the finch requires water daily, so
restricted water availability in the dry season provides the best opportunity for capture.
Typically, at each site I located possible watering points, e.g. dams, waterholes, creeks
or farm troughs. Once birds were observed at these watering points, I set up mist-nets
(mesh size 16 × 16mm, length 12m, 5 shelves; ECOTONE, Poland) nearby for two
consecutive days. Two to ten nets were used depending on the size and shape of the
watering points. All nets were generally opened half an hour before sunrise and closed
before noon, and were checked every 15 – 20 minutes. Each bird trapped in mist-nets
was immediately removed by licenced personnel and carefully placed in a breathable
cloth bag. It was important that each bag only contained one individual to minimise the
26
risk of disease transmission. I then put a metal band on the right tarsus of each bird for
future identification. These bands have unique codes and were supplied by the
Australian Bird and Bat Banding Scheme (ABBBS). All by-catch species were released
after banding. Blood samples were collected from all trapped Black-throated Finches
following a standard procedure (Appdendix A). For samples provided by K. Maute, red
cells were separated from fresh blood and stored in 90% ethanol instead of absorbing
whole blood with FTA© cards (Maute 2011).
LABORATORY PROCEDURE
DNA extraction
DNA was extracted from blood spots on FTA© cards and from blood cells using
ISOLATE Genomic DNA and ISOLATE II Blood DNA kits (Bioline, Australia),
respectively. I modified standard protocols by increasing incubation times
recommended by the manufacturer to yield large amounts (typically between 5-10μg) of
purified DNA (Appendix B and C). DNA quality and quantity were assessed by
electrophoresis (3V/min) on 1% agarose-TBE gels stained with GelGreenTM (Biotium,
Australia), followed by quantification using a NanoDrop ND-1000 spectrophotometer
(Thermo Scientific, Australia). I then diluted purified DNA extractions with nuclease-
free water to a standard concentration of approximately 5ng/μL for downstream
analysis.
Microsatellite genotyping
All individuals were genotyped at 18 microsatellite loci developed specifically for the
Black-throated Finch (Tang et al. 2014) (also see Chapter III). I performed three
27
multiplex PCRs using Type-it Microsatellite PCR kit (Qiagen, Australia) following
Tang et al. (2014) (see Chapter III), with the cycling conditions: initial 5 minute
denaturation at 95°C, 30 cycles of 95°C for 30 seconds; 60°C for 90 seconds and 72°C
for 30 seconds, with a final 30 minute extension at 60°C.
I purified PCR products using MicroCLEAN DNA Clean-up reagent (Microzone, UK),
following manufacturer’s instructions (Appendix D). DNA fragments were then
separated on an Applied Biosystems 3730xl Analyser with a LIZ600 internal size
standard at Georgia Genomic Facility (Athens, USA) and scored based on fragment
sizes using GENEMARKER version 2.2.0 (SoftGenetics, USA).
Mitochondrial DNA sequencing
In Chapter IV, in order to evaluate the effects of female-biased dispersal on the genetic
structure, I amplified a 396 base-pair segment of the mitochondrial DNA control region
in 5 individuals from each sampling region to represent the broad geographic area. The
exception to this was for the Townsville population where I selected 10 individuals to
account for multiple sampling locations in the area. I used previously published primers
paCRL1 (5’ -TGT-AGG-ATA-GCC-AAT-GTC-ATA-CG) and paCRH1 (5’ –CGC-
CTG-AAG-CCA-ATA-ACC-TA) (Rollins et al. 2012). These primers were designed
for the Long-tailed Finch (Poephila acuticauda) from the consensus sequence between
the Zebra Finch and the Black-throated Finch (Rollins et al. 2012). Each PCR reaction
(20μL) for the mtDNA sequence contained approximately 10ng of genomic DNA,
0.2μM of each primer (forward and reverse) and 1× Type-it Multiplex PCR Master Mix
(Qiagen, Australia). All samples were amplified in a C1000 Thermal Cycler (Bio-rad,
Australia) under the following PCR conditions: initial denaturation at 95°C for 5
minutes, 30 cycles of denaturation at 95°C for 30 seconds; annealing at 53°C for 90
28
seconds; extension at 72°C for 30 seconds, with a cycle of final extension at 60°C for
30 minutes. I checked the quality and quantity of PCR products with electrophoresis
(1.3V/min) on 1.5% agarose-TBE gels stained with GelGreenTM. Purification of the
PCR products and the Big Dye Terminator (BDT) sequencing reactions were completed
at the Australian Genomic Research Facility (AGRF), Brisbane, Australia. Products of
the BDT reactions were ethanol precipitated and run on an Applied Biosystems 3730xl
capillary sequencer at AGRF. All samples were sequenced in both the forward and
reverse directions to ensure accuracy of nucleotide identification.
I further screened all sequences in 4PEAKS version 1.7.2 (Nucleobytes Inc., The
Netherlands) for any misidentified base pairs in the sequences. Accepted sequences
were then aligned in MEGA version 6.06 (Tamura et al. 2013) and trimmed to a
uniform length of 302 base pairs. This sequence matched with the previously identified
control region sequence of other close-related species: P. acuticauda (99% match,
GenBank #JQ255398) and Erythrura gouldiae (87% match, GenBank #EF094896)
using the Basic Local Alignment Search Tool (BLAST –
http://www.ncbi.nlm.nih.gov/blast/). All 25 sequences were then submitted to GenBank
with accession numbers from KY610021 to KY610045.
29
CHAPTER III
Development, characterisation and cross-species amplification of 18
novel microsatellite markers for the endangered Black-throated Finch
(Poephila cincta) in Australia1
(Photo credit: L. Stanley Tang)
1 This chapter is published as:
Tang, L. S., C. Smith-Keune, M. G. Gardner, and B. D. Hardesty. 2014. Development, characterisation and cross-species amplification of 16 novel microsatellite markers for the endangered Black-throated Finch (Poephila cincta) in Australia. Conservation Genetics Resources 6:143-146.
30
ABSTRACT
The Black-throated Finch (Southern) (Poephila cincta cincta) is threatened by the
substantial landscape changes in northern Australia. I developed 18 polymorphic
microsatellite markers using 454-shotgun whole-genome sequencing technology. I
identified an average of 4.7 alleles per locus based on 63 wild caught individuals from
Townsville, Queensland. Thirteen and 9 markers were also successfully cross-amplified
in two confamilial species, the Double-barred Finch (Taeniopygia bichenovii) and the
Chestnut-breasted Mannikin (Lonchura castaneothorax) with 11 and 5 were
polymorphic, respectively. These markers will help understand the population genetic
structure of the endangered Black-throated Finch and determine genetic consequences
of landscape changes for the species.
31
INTRODUCTION
Approximately one-third of granivorous birds in northern Australia are in decline due to
the substantial landscape changes in the region (Franklin et al. 2005). The Black-
throated Finch (Poephila cincta) is one such declining species. In Australia, it is listed
as “Endangered” under the Environmental Protection and Biodiversity Conservation
Act 1999 (Commonwealth) nationally; as “Presumed Extinct” under the Threatened
Species Conservation Act 1995 in the state of New South Wales; and as “Vulnerable”
under the Nature Conservation Act 1992 in the state of Queensland.
There is little information on the population genetic structure and connectivity for most
of granivorous birds in Australia. Although landscape changes reduce the suitability of
the habitats of many granivorous birds, the lack of genetic information makes it difficult
to accurately examine the potential evolutionary impacts of human related habitat
changes on this fast declining group of birds. Limited marker are available for a few
species of granivores, e.g. the Zebra Finch (Taeniopygia guttata) (Forstmeier et al.
2007) and the Long-tailed Finch (Poephila acuticauda), (Rollins et al. 2012). High-
resolution markers are still lacking for most of the species.
Microsatellite DNA or simple sequence repeats are DNA sequences that contain
repeated motifs of one to six bases. They are found in all prokaryotic and eukaryotic
genomes with high levels of length polymorphism. Microsatellite markers have been
widely used in many fields including gene mapping, forensic studies, population
genetics and conservation biology (Zane et al. 2002). The development of these markers
traditionally involves isolation from partial genomic sequences of the target species and
screening several thousands of clones through hybridisation with repeat-containing
probes (Rassmann et al. 1991). Such traditional isolation protocols are laborious, costly
32
and inefficient, due to the fact that they need to be isolated de novo for most species.
Consequently, the strategy of designing “universal primers” is more problematic
limiting their potential (Zane et al. 2002). For example, in birds, the success rate of
cross-species amplification and polymorphism detection is only around 50% in species
that diverged between 10 and 20 million years ago (Primmer et al. 1996).
New technologies that utilise low-coverage whole-genome sequencing have emerged as
fast and cost-effective methods of isolating large numbers of markers suitable for
population and evolutionary genetic studies. One of these methods in particular, the
next generation sequencing, is in the process of taking over from the traditional
protocols as the preferred means for microsatellite developments, as it is particularly
beneficial for conservation biologists working with non-model taxa (Abdelkrim et al.
2009, Castoe et al. 2010). This method has several advantages over traditional
microsatellite development. First, the chance of missing out possible repeat motifs can
be greatly reduced, because traditional methods require only a few commonly identified
repeat motifs and they can vary widely amongst taxa (Dieringer and Schlotterer 2003).
Second, the confounding effects (over representation of fragments containing other
repetitive elements) of the recovered loci can be avoided because next generation
sequencing does not require the use of restriction enzymes to cut the DNA into
fragments, hence eliminating the chance of such enzymes cutting within transposable
elements of the DNA (Meglecz et al. 2007). Last, there is no need to edit sequence
chromatograms and it is quicker, cheaper and recovers more loci (Gardner et al. 2011).
In this chapter, I describe the development of 16 polymorphic nuclear microsatellite
markers for P. c. cincta using next generation sequencing technology. These markers
are useful to quantify population connectivity of P. cincta and to investigate the genetic
33
consequences of landscape changes. I also described preliminary tests of cross-species
amplification of these markers on two confamilial species, the Double-barred Finch
(Taeniopygia bichenovii) and the chestnut-breasted Mannikin (Lonchura
castaneothorax).
METHODS
The Black-throated Finch was sampled for next generation sequencing from the
Townsville region in North Queensland, Australia. I extracted and purified genomic
DNA (5µg) from one blood sample of P. c. cincta stored on Classic FTA® cards
(Whatman, USA) using ISOLATE Genomic DNA kits (Bioline, Australia) as per
manufacturer’s instructions. The DNA was shotgun sequenced on Roche 454 GS-FLX
instrumentation at Australian Genomic Research Facility, Brisbane, following Gardner
et al. (2011).
I screened the resulting sequences for perfect and compound microsatellite loci (di- to
hex-nucleotide motifs) with six or more repeats using the default settings of QDD
version 2.0 (Meglecz et al. 2010). Primers were designed in QDD version 2.0, using a
published PERL script that incorporates PRIMER3 software according to the following
parameter restrictions: pure repeats, primer size between 18 and 22, GC% between 40
and 60, PCR product size between 100 and 500bp, high 3’ stability and similar melting
temperature. I then synthesised the primers for the 40 highest quality loci through Life
Technologies Australia, with 5’ tailing (5’-GGTGGCGACTCCTGGAG-3’) to enable
indirect fluorescent labelling and to minimise costs.
Initially, I tested all 40 loci for amplification success and specificity in eight individuals
of P. c. cincta from geographically distinct locations, using the Type-it Microsatellite
34
PCR kit (Qiagen, Australia). I performed individual amplifications in 10μL reaction
containing 4-6ng DNA template, 1×Type-it Multiplex PCR Master Mix (Qiagen,
Australia), 0.2μM each primer (forward and reverse). Indirectly labelled reactions
contained the tailed forward primer and a reporter primer (5’ labelled with fluorescent
dye modification 6-FAM, VIC, NED and PET) at a 1:4 ratio (total = 0.2μM). All loci
were amplified in a C1000 Thermal Cycler (Bio-Rad, Australia) under the following
PCR conditions: initial 5 minutes of denaturation at 95°C, 30 cycles of 95°C for 30
seconds; 60°C for 90 seconds; 72°C for 30 seconds, with a final 30 minutes of extension
at 60°C. PCR products were visually checked for amplification success and whether
products were of expected sizes by electrophoresis through 2% agarose gel. I then
purified PCR products using MicroCLEAN DNA Clean-up Reagent (Microzone, UK),
following manufacturer’s instructions. DNA fragments were separated on an Applied
Biosystems 3730xl Analyser with a LIZ600 internal size standard at Georgia Genomic
Facility (Athens, USA) and scored using GeneMarker version 2.2.0 (SoftGenetics,
USA).
I synthesised directly fluorescent-labelled forward primers (6-FAM, VIC, NED and
PET) through Life Technologies Australia, for the selected loci to allow PCR
multiplexing of up to six loci (Table III-1). I genotyped these markers in multiplex
reactions for 63 P. c. cincta individuals caught near Townsville, Queensland. Multiplex
combinations were designed using Multiplex Manager 1.2 (Holleley and Geerts 2009)
and tested using PCR conditions described above with varied primer concentrations
(Table III-1).
I calculated number of alleles (N), observed (HO) and expected heterozygosities (HE),
and polymorphic information content (PIC) for each locus in CERVUS version 3.0
35
(Kalinowski et al. 2007). I tested for deviations from Hardy-Weinberg Equilibrium
(HWE) in GenAlEx version 6.5 (Peakall and Smouse 2012). Sequential Bonferroni
corrections were used to correct for multiple comparisons (Benjamini and Hochberg
1995). I also investigated potential linkage disequilibrium between pairs of loci using
GENEPOP version 4.2 (Rousset 2008) and screened for potential null alleles in
MICRO-CHECKER version 2.2.3 (Van Oosterhout et al. 2004).
I also tested all 18 loci in eight individuals of T. bichenovii and five individuals of L.
castaneothorax from the Townsville region. A positive control containing DNA from
the original species was used in each experiment. I extracted DNA from blood samples
of both species using the same protocol as described above. All PCR and genotyping
protocols of cross-species testing were the same as above.
RESULTS AND DISCUSSION
The initial shotgun sequencing produced 128,854 reads with an average length of 285bp
and total GC content of 42.7%. There were 1,177 microsatellite loci identified after the
initial screening in QDD version 2.0 and PCR primers were successfully designed for
all.
Genotyping of the 40 best microsatellite loci resulted in 18 loci that had interpret-2 peak
profiles. I found all these 18 loci were in HWE, except for btfi27, and one pair of loci
(btfi38 and btfi40) exhibited significant linkage disequilibrium after Bonferroni
corrections. Null alleles may be present at loci btfi04 and btfi27 as indicated by
homozygote excess. Marker btfi23 contained one base pair alleles possibly due to indel
mutations in the flanking region. Loci 31 and 31-2 were co-amplified and possibly
contain over lapping alleles. P. c. cincta polymorphic markers exhibited 2 to 11 alleles
36
per locus with an average of 4.7 (± 0.72 standard error). All markers displayed an
average expected heterozygosity of 0.47 (± 0.075 standard error).
Lengths of loci in cross-species amplifications were typically very similar across the
species tested. Amplification was high for all multiplex suites, although levels of
polymorphism varied. Thirteen loci amplified successfully for T. bichenovii, and 11
were polymorphic with 2 to 9 alleles per locus (Table III-2). Nine loci amplified
successfully for L. castaneothorax and 5 were polymorphic with 2 to 5 alleles per locus
(Table III-2). Transferability of P. cincta markers was moderately high (11 out of 18) in
T. bichonovii, indicating these markers are likely to be informative for population
studies within the genus Taeniopygia. On the contrary, the low level of transferability (9
out of 18) of these markers in L. castaneothorax suggests their limited suitability for
population studies in the genus Lonchura. This highlights the potential utility of these
markers across other related grassfinch species (Estrildidae), although further
optimisation may be required.
Tropical and subtropical grassy woodlands are under increasing threat from habitat loss
and fragmentation as a result of agricultural, industrial and urban development in
northern Australia. The Black-throated Finch that utilises such habitats faces continuing
population decline. The newly developed microsatellite markers herein will provide a
useful tool to examine the genetic structure and connectivity, to monitor the genetic
diversity, and to detect inbreeding depression in the species so that appropriate
conservation management strategies can be implemented.
37
Table III-1 Details for 18 Black-throated Finch (Poephila cincta cincta) microsatellite loci developed from 454 shotgun sequences
Locus Repeat motif Primer sequence (5’ – 3’) Primer conc. (μM)
Size range (bp)
N NA HO HE PHWE PIC GenBank accession no.
btfi01B (AT)7 [VIC]CAAGCAGTCACAGAACAACCA GACATATCCAAGGAAGCCAAA 3 108-116 63 2 0.032 0.031 0.898 0.031 KF366586
btfi02C (ACG)9 [PET]GTTGTTGGTCTGCTGGGACT CCTCTGACAGCAGGACAAGG 4 105-120 63 6 0.667 0.736 0.082 0.698 KF366587
btfi04B (AG)7 [NED]TGTCTGCAGCACTTCCAAAG AGACCATCTGGAGCTTGTGC 3 201-207 62 4 0.387 0.534 0.007 0.477 KF366588
btfi05A (AGT)10 [PET]CCAAATGGGACAGAGTGGAT CTCAGCCTCGCTTTGCTTC 5 169-217 54 10 0.685 0.734 0.999 0.707 KF366590
btfi06C (AG)7 [6-FAM]CTCAGCCACTCTGGCAAACT TTGGCCAGAGAATAAGCTGC 2 163-165 63 2 0.032 0.031 0.898 0.031 KF366591
btfi09A (AGC)7 [VIC]TTTGTGCCAGAAAGCTGGAT ACTCCTGAGCAGTGGTCCTG 3 123-129 54 3 0.648 0.593 0.608 0.512 KF366592
btfi22A (AAAT)10 [NED]TTTGAGAATGGCCTGGTTAGA CCTGCTTCCAGTTACCATGC 3 140-160 54 6 0.63 0.667 0.864 0.603 KF366594
btfi23B§ (AC)9 [6-FAM]GAATGCTCCCACATCTCCTG CTCCTGTTTGCTGAGGGAAA 4 410-418 63 5 0.746 0.620 0.369 0.566 KF366600
btfi27B (AGAGGG)7 [PET]ACCCTGAGGAGTGGAAGGAG CCAAGCCTTGCCATCTATTG 3 146-200 60 6 0.350 0.659 0.000 0.6 KF366589
btfi31C* (AG)10 [PET]CCTCCACAAATGTCTAGGGAA ATCCCAGCATACTCCCGAA 4 214-226 60 2 0.067 0.065 0.789 0.062 KF366593
btfi31-2C* (AG)10 [PET]CCTCCACAAATGTCTAGGGAA ATCCCAGCATACTCCCGAA 4 238-246 63 2 0.063 0.062 0.795 0.06 KF366593
btfi34A (AAT)6 [6-FAM]TGGGTTATACAGCCCAGAGG CGTTGTCTGTCTTCAGTCCCA
1.5 168-171 54 2 0.222 0.199 0.358 0.178 KF366595
btfi36A (AAT)6 [6-FAM]GGCATTCCTGTCTTAGGAATCA GACACCGTCCTCCTGTAACG
3 337 54 1 - - - - KF366596
btfi38B (AAAAC)6 [6-FAM]GGCTGAGCTCTCACCAACAG CTTCAGGAGCACATTGAGCA
1.5 205-245 63 7 0.841 0.756 0.076 0.712 KF366597
btfi39C (ACAG)6 [6-FAM]GGAGCAGATGGAGAGCTGAG GGTGTCCTTCACTGTGTCCC
2 244-260 63 5 0.73 0.671 0.691 0.603 KF366598
btfi40C (ACCT)7 [VIC]TTGCATACATCAATGTCAGGTG TGCTTATCACCAAAGCAGCA 2 130-170 63 11 0.905 0.866 0.100 0.844 KF366599
btfi41A (AGG)6 [6-FAM]TCCTAGTTCTGGCAGACCCTT ATCTCTGTCATGTCGGTCCC
2 224-230 54 2 0.204 0.316 0.010 0.264 KF366601
btfi43B (AGT)8 [VIC]TCATCCACCTGTCACACCAG AGCAGCTCCAGTTGCCTGTA 2 323 63 1 - - - - KF366602
N – number of individuals amplified; NA – number of alleles; HO – observed heterozygosity; HE – expected heterozygosity; PHWE – significance value of Hardy-Weinberg Equilibrium at P < 0.0033 after FDR correction; PIC – polymorphic information content A, B, C Multiplex systems * Loci btfi31 and btfi31-2 were co-amplified with the same set of primers and may contain over lapping alleles § Marker btfi23 contains out-of-phase (1 bp) alleles
38
Table III-2 Cross-species amplification success (number of scorable individuals out of total individuals amplified), allele size range in base pairs (bp) and number of alleles (NA) for the novel Black-throated Finch (Poephila cincta cincta) microsatellite loci in the Double-barred Finch (Taeniopygia bichenovii) and Chestnut-breasted Mannikin (Lonchura castaneothorax)
Locus Success Size Range (bp) NA Locus Success Size Range (bp) NA Taeniopygia bichenovii Lonchura castaneothorax
btfi01 0/8 - - btfi01 0/5 - - btfi02 8/8 105-114 4 btfi02 5/5 102-114 5 btfi04 8/8 199-201 2 btfi04 5/5 199-201 2 btfi05 7/8 181-235 9 btfi05 0/5 - - btfi06 8/8 163 1 btfi06 5/5 163 1 btfi09 8/8 120-123 2 btfi09 5/5 123 1 btfi22 0/8 - - btfi22 0/5 - - btfi23 7/8 406-416 4 btfi23 0/5 - - btfi27 6/8 134-170 5 btfi27 5/5 164-170 3 btfi31 7/8 212-218 3 btfi31 4/5 212-218 2 btfi31-2 0/8 - - btfi31-2 0/5 - - btfi34 8/8 168-180 4 btfi34 5/5 168 1 btfi36 5/8 337 1 btfi36 0/5 - - btfi38 0/8 - - btfi38 0/5 - - btfi39 0/8 - - btfi39 0/5 - - btfi40 6/8 158-190 6 btfi40 0/5 - - btfi41 8/8 227-239 5 btfi41 5/5 227 1 btfi43 8/8 311-326 4 btfi43 5/5 311-329 4
39
CHAPTER IV
Genetic structure and diversity of the Black-throated Finch (Poephila
cincta) across its current distribution2
(Photo credit: L. Stanley Tang)
2 This chapter is published as: Tang, L. S., C. Smith-Keune, A. C. Grice, J. M. Moloney and B. D. Hardesty. In Press. Genetic structure and diversity of the black-throated finch (Poephila cincta) across its current range. Australian Journal of Zoology.
40
ABSTRACT
Understanding the patterns of population connectivity and level of genetic diversity can
facilitate the identification of both ecologically relevant populations and the spatial
scales at which conservation management may need to focus. I quantified genetic
variation within and among populations of Black-throated Finches across their current
distribution. To quantify genetic structure and diversity, I genotyped 242 individuals
from four populations using 15 polymorphic microsatellite markers and sequenced 25
individuals based on a 302 base-pair segment of mitochondrial control region. I found
modest levels of genetic diversity (average allelic richness r = 4.37 ± 0.41 standard
error and average heterozygosity HO = 0.42 ± 0.040 standard error) with no bottleneck
signature among sampled populations. I identified two genetic groups that represent
populations of two subspecies based on Bayesian clustering analysis and low levels of
genetic differentiation based on pairwise genetic differentiation statistics (all FST, RST
and Nei’s unbiased D values < 0.1). Our data suggest that genetic exchange occurs
among sampled populations despite recent population declines. Conservation efforts
that focus on maintaining habitat connectivity and increasing habitat quality to ensure a
high level of gene flow on a larger scale will improve the species’ ability to persist in
changing landscapes. I also suggest that conservation management should support
continuous monitoring of the bird to identify any rapid population declines as land use
intensification occurs throughout the species’ range.
41
INTRODUCTION
Quantifying genetic diversity and connectivity in declining populations
Genetics contributes to conservation of biodiversity at both species and ecosystem
levels (McNeely et al. 1990, Frankham 2010a). At the species level, genetic factors
affect the risk of population or species extinction through inbreeding depression, loss of
genetic diversity and decreased evolutionary potential (Frankham 2005). Genetic
variability also contributes to the survival, functioning and diversity of all ecosystems
(Reusch et al. 2005, Crutsinger et al. 2006). Therefore, applying molecular genetics to
acquire vital information, including effective population size, demographic history,
population structure, gene flow and genetic diversity, for species conservation has
become one of the primary goals of conservation genetics (Frankham 2010a).
Small, isolated populations may have an increased extinction risk due to environmental,
demographic and genetic stochasticity (Lande 1988). Habitat loss and fragmentation
may lead to further isolation of small populations in habitat remnants, decreasing the
viability of the metapopulation (Hanski and Ovaskainen 2000). For example, a lack of
habitat connectivity and suitable breeding habitat in human impacted landscapes has led
to the decline of the endangered Black-capped Vireo (Vireo atricapilla) in the USA. As
a result, lower genetic diversity and increased differentiation have been observed in
current populations compared with historical samples, indicating the significant impact
of habitat fragmentation (Athrey et al. 2012).
Additionally, population fitness is likely to be lost both through increased inbreeding
depression at the individual level and through loss of genetic diversity at the population
level (Frankham 2005). Studies of the Glanville Fritillary Butterfly metapopulation in
Finland, for example, have demonstrated that inbreeding contributes to the extinction of
42
wild populations. Inbreeding reduced the larval survival of the butterflies by reducing
the egg hatch rate and increasing the pupal period, increasing the chance that pupae
would be parasitised by wasps. Inbreeding also shortened the lifespan of the female,
leading to reduced egg production (Saccheri et al. 1998). Overall, the adaptability of
small, isolated populations to environmental changes is reduced through decreased
population viability and fitness, hence increasing the extinction risk.
For populations of conservation concern, it is important to understand the pattern and
degree of genetic connectivity and diversity, so that appropriate conservation
management strategies can be implemented. For example, British populations of the
Black Grouse (Tetrao tetrix) have become fragmented and isolated into three
genetically distinct populations with little migration between them. The genetic
differentiation observed is likely due to genetic drift in small and recently fragmented
sub-populations. Given that isolation has occurred recently, it is appropriate to manage
these populations as a whole (Höglund et al. 2011). Accurate estimation of genetic
diversity may also help avoid adverse effects such as underestimated extinction risk,
inappropriate recovery strategies (including reintroduction) and mixing of different
evolutionarily significant units with reduced fitness (Frankham 2005). For example, in
the absence of genetic data, the reintroduced populations of the Koala (Phascolarctos
cinereus) in south-eastern Australia were from a highly inbred island population. As a
result, they have suffered low genetic diversity and fitness, including poor sperm quality
and high frequency of testicular aplasia (Seymour et al. 2001).
Habitat change and the decline of granivorous birds in Australia
In Australia, habitat alteration and landscape changes have resulted in the decline of
many faunal groups, including granivorous birds, and appropriate conservation
43
management strategies are urgently needed to ensure population and species persistence
(Lindenmayer 2009). Granivorous birds represent more than a quarter of all threatened
land bird taxa in Australia (Garnett and Crowley 2000b), and substantial landscape
changes have resulted in the dramatic decline in these birds in recent decades. The
tropical and subtropical savannahs of northern Australia, which support the largest
granivorous bird assemblages, have experienced the greatest declines (State of the
Environment Advisory Council 1996, Franklin et al. 2005).
The Black-throated Finch (Poephila cincta) is a granivorous bird endemic to the mixed
woodlands habitat of Australia. Since 1998, the southern form has not been seen from
central to north Queensland and extensive field surveys and sighting records suggest
that the species may have declined by 50% in the past 20 years (Environment Australia
2000, Black-throated Finch Recovery Team 2007). The decline of the Black-throated
Finch began early in the 1900s with the expansion of pastoralism into its habitat and is
associated with substantial landscape changes in the region (Franklin et al. 2000).
The movement patterns of the bird are poorly known. The available evidence from
surveys and sighting records suggest that the bird is sedentary with only limited
movements in response to food availability or post-breeding dispersal (Baldwin 1976,
Mitchell 1996, Natural Resource Assessment Environmental Consultants 2007,
Forshaw et al. 2012). In addition, based on recorded sightings, the distribution of P.c.
cincta now appears to be severely fragmented and populations continue to decline. It is
therefore likely that the existing populations of the bird are at risk of losing genetic
diversity with potentially negative implications for adaptation to future environmental
changes.
44
In this study, I provide the first quantitative measure of the pattern and degree of
population connectivity and genetic diversity of the Black-throated Finch across its
current range. Specifically, I examined the genetic diversity of four finch populations
from central Queensland, northern Queensland, far northern Queensland and Cape York
Peninsula. I also used individual and population based approaches to delineate the
genetic structure and to estimate the level of gene flow within and among these
populations. I hypothesised that: (1) genetic diversity within populations is low; (2)
recent population contraction/genetic bottleneck is likely to be present; (3) populations
are likely to show distinct genetic structures with low levels of gene flow between them;
and (4) genetic differentiation between two subspecies is likely to be significant.
METHODS
Field sampling and genetic procedures
Sample collection
On the basis of sightings records in the past five years, I sampled sites across the current
range where the Black-throated Finch had been seen regularly (Chapter II). The
southern form (P. c. cincta) was sampled in the Townsville region as well as in the
Desert Uplands in central Queensland. The northern form (P. c. atropygialis) was
sampled at Mareeba in far north Queensland. I collected small amount of blood (30-
50μL) from each individual trapped using mist nets following strict protocols. Blood
samples were applied to FTA© cards and dried for further analysis (Appendix A).
Samples from Lakefield National Park on Cape York Peninsula were obtained from a
previous study (Maute 2011) and were stored as blood cells in 70% ethanol.
45
DNA extraction and genotyping
DNA was extracted from blood spots on FTA© cards and from blood cells using
ISOLATE Genomic DNA and ISOLATE II Blood DNA kits (Bioline, Australia),
respectively. Protocols were modified by increasing incubation times as recommended
by the manufacturer to yield large amounts (5-10μg) of purified DNA (Appendix B and
C).
All individuals were genotyped at 18 microsatellite loci developed specifically for the
Black-throated Finch (Tang et al. 2014). Microsatellite genotyping procedure is detailed
in Chapter II.
I amplified a 396 base-pair segment of the mitochondrial DNA control region in 5
individuals from each region to represent the broad geographic area. The exception to
this was for the Townsville population where I selected 10 individuals to account for the
genetic differentiation within this region. Details of the mitochondrial DNA sequencing
are in Chapter II.
Statistical Analysis
Marker characteristics
All microsatellite genotype data were checked for evidence of typographic and scoring
errors (large allele dropout, stuttering and the presence of null alleles) for each
population using MICROCHECKER version 2.2.3 (Van Oosterhout et al. 2004). I also
performed neutrality tests in POPGENE version 1.31 (Yeh and Boyle 1997) for
microsatellite markers and in DnaSP version 5.10.1 (Librado and Rozas 2009) for
mitochondrial DNA to confirm that all selected markers evolved neutrally across
populations.
46
I analysed all microsatellite loci to test for departures from Hardy-Weinberg equilibria
(HWE) within each population by performing global χ2 goodness-of-fit tests across
population-specific FIS (inbreeding coefficient) estimates, and linkage disequilibrium
(LD) between all pairs of loci in GENEPOP version 4.3 (Rousset 2008). I used Markov
Chain Monte Carlo (MCMC) algorithms to estimate the probabilities of all tests with
10,000 dememorisation steps, 1,000 batches and 5,000 iterations per batch (Guo and
Thompson 1992, Slatkin and Excoffier 1996). A sequential Bonferroni correction was
also applied to all probability values of multiple comparisons to reduce Type I statistical
error (Zar 1999).
Genetic variability
For microsatellite markers, I calculated the observed heterozygosity (HO), the expected
heterozygosity (HE), and the number of private alleles (NPA) in GenAlEx version 6.5
(Peakall and Smouse 2006, 2012). NPA is often used to estimate the amount of gene flow
(Nm, the number of migrants) between local populations (Slatkin and Barton 1989). The
average allelic richness (number of alleles, r), allele frequencies and average genetic
diversity (GD) were calculated in FSTAT version 2.9.3.2 (Goudet 1995). The method
used to calculate r incorporates a rarefaction method to compensate for unequal sample
sizes (el Mousadik and Petit 1996), as is appropriate given our sampling design.
Population-specific FIS values were calculated and tested for statistical significance by
randomising alleles within populations in FSTAT version 2.9.3.2 in order to identify
deviations from HWE and potential substructuring within populations (Wahlund 1928).
I examined genetic diversity for the mitochondrial DNA sequence by calculating the
number of haplotypes (NH), haplotype diversity (h) and nucleotide diversity (π) in
DnaSP version 5.10.1. The haplotype richness (HP) was estimated in CONTRIB version
47
1.02 (Petit et al. 1998) with a rarefaction method to compensate for unequal sample
sizes. I also manually counted the number of haplotypes that were found in only one
locality (private haplotypes, NPH) to estimate the amount of gene flow between local
populations.
I used two traditional ad hoc methods to test for signatures of recent genetic bottlenecks
within each population based on microsatellite data. First, heterozygosity excess was
tested assuming a two-phase mutation (TPM) model with 95% stepwise mutations, 5%
multiple-step mutations, and a variance among multiple steps of 12, in BOTTLENECK
1.2.02 (Piry et al. 1999). The TPM model is considered to be the most appropriate for
microsatellite data (Piry et al. 1999). Significance of heterozygosity excess over all loci
was determined with a one-tailed Wilcoxon sign rank test as implemented in the
program. Second, I used the M-ratio method developed by Garza and Williamson
(2001) to calculate the ratio (M) of the total number of alleles to the range in allele
sizes, and its critical value MC (5% of values fall below MC as determined by
simulations). M and MC were estimated using M_P_VAL and CRITICAL_M,
respectively (Garza and Williamson 2001). I assigned three required parameters that
were required for both programmes with recommended values: (1) pre-bottleneck θ =
4Neμ = 10 (where Ne = effective population size, μ = mutation rate); (2) the percentage
of one step mutations, ρs = 0.9; and (3) the mean size of non one-step mutations, Δg =
3.5. Each set of simulations consisted of 10,000 iterations.
Genetic structure
I analysed the genetic structure of all populations using four complementary approaches
to increase the reliability of detecting genetic signals. First, I performed analyses of
molecular variance (AMOVA) using allele frequencies to examine the genetic structure
48
of four populations when grouped into two subspecies (P. c. cincta: TSV and CEQ; P.
c. atropygialis: FNQ and CYP) and without grouping. The significance of the analysis
was tested running 10,000 permutations in ARLEQUIN version 3.5.1.3. (Excoffier and
Lischer 2010).
Second, pairwise genetic differentiation among 4 a priori Black-throated Finch
populations was estimated using statistics based on both an infinite allele model (IAM)
(FST) and a stepwise mutation model (SMM) (RST). Both statistics were calculated in
ARLEQUIN version 3.5.1.3. The significance of FST values was tested running 10,000
permutations and corrected with a sequential Bonferroni method for multiple tests.
There is no direct probability test for RST. I used the estimated Nm as an indirect measure
to quantify the significance of differentiation (Slatkin 1995). Specifically, if Nm ≤ 1, I
considered there was no gene flow between two populations, whereas Nm ≥ 3 indicated
a high level of gene flow (Wang et al. 2001). I also calculated the unbiased genetic
distance D (Nei 1978) to estimate the genetic relationship among populations.
Third, I assessed the population genetic structure and individual admixture based on
microsatellite variation using the Bayesian clustering approach implemented in
STRUCTURE 2.3.4 (Pritchard et al. 2000). This programme uses an MCMC algorithm
to infer distinct genetic clusters (k), estimate allele frequencies in each cluster and
allocate population memberships of each individual. I chose an admixture model,
assuming allele frequencies are correlated among populations. Trial runs without
location as prior produced weak genetic differentiation with no distinct clusters.
Therefore, I used an individual-based approach with sampling locations as prior. The
simulations were run for 10 replicates at each k value ranging from 1 to 10. I chose the
upper limit of k to be greater than the number of sampled populations to account for any
49
distinct genetic clusters that might be present within each population. Each replicate
consisted of a burn-in period of 100,000 MCMC steps, followed by 2 × 105 iterations.
Trial runs indicated that run durations were sufficient for likelihood values to stabilise.
The best supported k values were determined by posterior probabilities (P(D)) and Δk
method (Evanno et al. 2005) in STRUCTURE HARVESTER version 0.6.94 (Earl and
vonHoldt 2012). The clusters output of the independent runs of the best supported k
values were permuted and aligned using the “Full Search” algorithm to minimise the
effects of “label switching” and “multimodality” in CLUMPP version 1.1.2 (Jakobsson
and Rosenberg 2007). The output files were then visualised in DISTRUCT (Rosenberg
2004).
Last, I constructed a haplotype genealogy for the mitochondrial DNA control region in
HAPLOVIEW (Barrett et al. 2005) using the topology of most parsimonious trees
constructed in DNAPARS implemented in PHYLIP package version 3.695 (Felsenstein
2005). I used bootstrapping as the resampling method with 500 replicates to produce the
consensus tree. The aligned sequence of the control region of the Gouldian Finch
(GenBank #EF094896) was used to produce a rooted network.
RESULTS
Marker characteristics
There was no evidence for scoring errors associated with stuttering, large allele dropout
or null alleles at any of the 18 microsatellite loci scored. Neutrality tests confirmed that
all of the microsatellite and mitochondrial DNA markers were neutral. Markers btfi43
50
and btfi36 were monomorphic and marker btfi23 contained 1base pair of out-of-phase
alleles. Because of this, they were not used in further analysis. After Bonferroni
corrections (corrected α = 0.0031), HO deviations from the Hardy-Weinberg
Equilibrium (HWE) were detected in at least one population at 5 loci (btfi04, btfi27,
btfi31-2, btfi40 and btfi41). However, only locus btfi27 showed consistent deviation
across all populations and this locus was therefore excluded from further analysis.
Linkage disequilibria of each pair of the remaining 14 loci were not detected within or
among populations after Bonferroni corrections (corrected α = 0.0005).
Genetic variability
Microsatellite markers showed a modest level of polymorphism. The number of alleles
ranged from 2 (btfi36) to 14 (btfi05), with an average of 4.367 (± 0.412 standard error)
alleles per locus over all populations. The highest number of private alleles was
detected in the TSV population (NPA = 6); whereas the CEQ and CYP populations had
only 3 private alleles each. No private alleles were detected in FNQ probably due to the
small sample size of the population. The observed heterozygosity ranged from 0.373 to
0.444, with an average of 0.417 (± 0.040 standard error) across all populations. The
expected heterozygosity exhibits a similar level of variation with an average of 0.429 (±
0.039 standard error) across all populations. Both the genetic diversity index (GD) and
the allelic richness (r) showed few differences between populations. The inbreeding co-
efficient (FIS) was also relatively low in all populations, indicating no significant
heterozygosity deficiency (Table IV-1).
The control region of the mitochondrial DNA sequence data identified 5 polymorphic
sites with two types of transitions: A-G (site 89, 135, 221 and 281) and T-C (site 241).
These polymorphic sites defined 6 haplotypes, only one of which was common across
51
all populations (Hap_BA). However, the frequencies of Hap_BA were not evenly
distributed within populations; it was highest in TSV with a frequency of 0.44 across all
individuals sampled and lowest in CEQ with a frequency of 0.13 across all individuals
sampled. Three haplotypes were found only in one population (Table IV-2). The overall
haplotype diversity (h) was 0.587 (± 0.022 standard error) and the overall haplotype
richness was 2.664. The nucleotide diversity was low (π = 0.003 ± 0.001 standard
error). All identified haplotypes are stored in GENBANK at accessions KR676617 –
KR676622.
Table IV-1 Measurements of genetic diversity for each of the Black-throated Finch (Poephila cincta) populations. NPA: number of private alleles; HO: observed heterozygosity; HE: expected heterozygosity; r: average allelic richness; GD: genetic diversity index; FIS: inbreeding coefficient
Region NPA HO (SE) HE (SE) r (SE) GD (SE) FIS
TSV 6 0.41±0.08 0.42±0.08 3.98±0.67 0.45±0.08 0.04 CEQ 3 0.37±0.08 0.40±0.08 3.84±0.66 0.43±0.08 0.08 FNQ 0 0.44±0.07 0.43±0.07 3.57±0.63 0.47±0.07 0.01 CYP 3 0.44±0.08 0.46±0.08 4.18±0.75 0.50±0.08 0.04
Table IV-2 Counts of six identified mitochondrial haplotype types in each population of the Black-throated Finch (Poephila cincta). Relative frequencies within each population shown in brackets
Region Hap_BA Hap_BB Hap_BC Hap_BD HapBE HapBF TSV 7 (0.7) 1 (0.1) 2 (0.2) - - - CEQ 2 (0.4) - - 2 (0.4) 1 (0.2) - FNQ 4 (0.8) - - - - 1 (0.2) CYP 3 (0.6) - - - 1 (0.2) 1 (0.2)
There was no evidence for a significant genetic bottleneck detected in any populations
using either the M-ratio or heterozygosity excess methods. Across all populations, the M
ratios ranged between 0.903 and 0.964. These ratios were consistently higher than the
MC values. One-tailed Wilcoxon sign rank tests showed no significant heterozygosity
52
excess in any of the populations (all probability values were greater than 0.05) (Table
IV-3).
Table IV-3 Result comparison of the two ad-hoc bottleneck analysis in each of the Black-throated Finch (Poephila cincta) population. M: ratio of total number of alleles to the range of allele sizes; MC: critical value of M; PW: probability value of one-tailed Wilcoxon sign rank test for heterozygosity excess. For M ratio method, if M < MC; and for Heterozygosity excess method, if PW < 0.05, a recent bottleneck is likely to be present
Region M MC Presence of bottleneck (M ratio)
PW Presence of bottleneck (Heterozygosity excess)
TSV 0.9034 0.7513 No 0.7492 No CEQ 0.9644 0.7147 No 0.9119 No FNQ 0.9111 0.6534 No 0.5270 No CYP 0.9350 0.7200 No 0.9161 No
Genetic structure
AMOVA results based on grouping populations of the Black-throated Finch into two
subspecies revealed significant differences between subspecies (P = 0.013 < 0.05),
between populations within subspecies (P < 0.0001) and within populations (P <
0.0001). In all cases, most of the genetic variation (94.57%) was observed within
populations (Table IV-4). The significant genetic differences between population pairs
were further supported by FST estimates after Bonferroni corrections (corrected α =
0.0083), except for FNQ and CYP. However, differences were generally weak with low
FST values (mean FST = 0.045, ranging between 0.014 and 0.071) indicating low neutral
genetic differentiation between all of the populations. This is reflected in low RST values
(mean RST = 0.048, ranging between 0.014 and 0.077) with high levels of gene flow
inferred (mean Nm = 15.08, Table IV-4). Nei’s unbiased genetic distance (D) showed
that populations of TSV and FNQ were the most different, followed by FNQ and CEQ.
53
The TSV and CEQ populations were the most similar, followed by FNQ and CYP
(Table IV-4).
Table IV-4 Analysis of molecular variance (AMOVA) grouping populations of the Black-throated Finch according to subspecies (Poephila cincta cincta and P. c. atropygialis).
Source of variance df Sum of squares
Variance components
Percentage of variation
Between subspecies 1 32.988 0.115 3.36 (FCT = 0.034, p < 0.001)
Between populations within subspecies
2 19.010 0.070 2.04 (FSC = 0.021, p < 0.001)
Within populations 480 1544.610 3.226 94.57 (FST = 0.054, p = 0.013)
Total 483 1596.608 3.410 100
Furthermore, the individual based Bayesian clustering analysis in STRUCTURE
identified two distinct genetic clusters (k = 2, Figure IV-1) with a priori population
information. Roughly, FNQ and CYP belonged to one cluster, TSV and CEQ another,
mirroring the differentiation between two subspecies of the Black-throated Finch. The
maximum posterior probability ln P(D) also corresponded to k = 2 when population
priors were inferred (Figure IV-2). This eliminated the possibility of a single genetic
cluster of all Black-throated Finch populations.
54
The parsimony network of haplotypes identified only one major haplotype group
connected with 5 small haplotype groups diverged by only one transition each (Figure
IV-3). The overall small number of haplotypes identified among selected individuals
indicated low levels of genetic (haplotype) diversity. The unclear haplotype structuring
suggested a lack of historical genetic isolation between the four groups of the black-
throated finch.
Figure IV-2 Individual membership coefficients derived from Bayesian inference of genetic structure across four Black-throated Finch populations using admixture model with (bottom, k = 2) a priori populations. A single vertical line represents an individual
Figure IV-1 Log posterior probabilities (ln P(D), left) and Δk (right) of the microsatellite data for each number of genetic clusters (k) tested
55
DISCUSSION
To infer appropriate conservation management strategies, I examined the genetic
diversity and structure of the endangered Black-throated Finch populations in Australia
by using both microsatellite and mitochondrial DNA markers from more than 200
samples collected between 2009 and 2013. Genetic analyses provided evidence that the
neutral genetic diversity within each population is modest. Genetic differentiation
between two recognised subspecies was also observed, which has important
management implications. This work provides information useful for the management
and conservation of the endangered Black-throated Finch.
Figure IV-3 Mitochondrial DNA haplotype network of the selected Black-throated Finch samples from each population. Circle size indicates proportion of individuals assigned that haplotype. Solid lines connecting haplotypes are proportional to the number of mutations separating them. The network is rooted to the Gouldian Finch (GOFI)
56
Genetic diversity
The levels of observed genetic diversity within the sampled populations of the Black-
throated Finch are modest (r ranged from 3.57 to 4.18; HO ranged from 0.37 to 0.44).
This contrasts with the situation in the more common and widespread congener, the
Long-tailed Finch (Poephila acuticauda), which on average has 16.4 alleles per locus
and an average heterozygosity of 0.71 based on 12 microsatellite markers (Rollins et al.
2012). The use of both allelic richness and average heterozygosity identified that birds
in TSV and CYP had a higher level of genetic diversity than those in CEQ and FNQ.
Despite the small sampling size from CEQ, I have discovered several hundred Black-
throated Finch in the region towards the end of the study. This population is probably
larger than that from TSV. Therefore, there is no association between population sizes
and the variation of genetic diversity.
The Black-throated Finch is considered sedentary and its current distribution is highly
fragmented (Forshaw et al. 2012). Accordingly, neutral genetic diversity is expected to
be lower than it would have been had the populations been mobile and in a continuous
landscape, due to small or isolated populations resulting in stochastic loss of alleles and
more rapid genetic drift. In birds, populations are generally considered to have low
genetic diversity if the average number of microsatellite alleles is less than 3-4 and the
average heterozygosity is below 0.5. For example, the endangered Black Robin
(Petroica traversi) from New Zealand was reported to have one of the lowest
heterozygosity values (0.2) among avian species, which confirmed the extreme low
level of genetic diversity and history of severe population bottleneck of the species
(Ardern and Lambert 1997). On the other hand, widely dispersed common species with
high levels of genetic diversity, such as the House Sparrow (Passer domesticus)
demonstrate allelic richness of 13.6 and average heterozygosity of 0.8 based on 8
57
microsatellite loci from multiple locations in North America, Europe and Africa (Schrey
et al. 2011). Therefore, the levels of observed genetic diversity within sampled Black-
throated Finch populations are modest compared with that of other species.
Genetic bottleneck
I hypothesised that genetic bottlenecks were likely to be present as a result of isolation
and population declines. However, both heterozygosity excess and M ratio analyses
identified no signature of bottleneck in the sampled Black-throated Finch populations.
Many studies have suggested that endangered populations do not necessarily have to
experience recent bottleneck events to show low levels of neutral genetic diversity. For
example, the extremely low levels of genetic diversity in the Madagascar Fish Eagle
(Haliaeetus vocifer) is due to historically small population sizes (over thousands of
years) rather than recent population bottlenecks (Johnson et al. 2009). Similarly, other
vertebrate species, including the Wandering Albatross (Diomedea exulans) (Milot et al.
2007), the Kangaroo Rat (Dipodomys heermanni morroensis) (Matocq and Villablanca
2001), and the Brown Bear (Ursus arctos) (Paetkau et al. 1998) also show low levels of
genetic diversity without signatures of recent bottlenecks, in spite of experiencing
recent habitat loss.
The continuance of low genetic diversity without signatures of recent bottlenecks is
likely due to the accumulation of nucleotide substitutions by low annual fecundity of the
species, or due to a reduced rate of mutation by increased body size and slower
metabolic rates (Gillooly et al. 2005, Milot et al. 2007). However, the Black-throated
Finch is a small bird that has a relatively fast metabolic rate with high annual fecundity.
Its fast reproductive cycle also increases the rate of mutation. Moreover, the Black-
throated Finch was once widespread. It is possible that sampled populations did not
58
experience significant decline until very recent years and genetic signatures of the
population contraction are not yet evident in the sampled genetic markers.
Genetic isolation
Genetic analyses using microsatellite DNA in this study suggests that the current
populations of the Black-throated Finch are substantially different, particularly between
northern and southern forms. Subspecies genetic structure is also present in its
congeneric Long-tailed Finch, for which the strong genetic separation occurs between
regions due to a biogeographic barrier, the Ord Arid Intrusion in Western Australia
(Rollins et al. 2012). However, the Black-throated Finch was historically distributed
across a more or less continuous landscape to the northwest of the Burdekin Gap in
northern Australia with both subspecies co-occurring north of Townsville. Hence, little
evidence suggests that a geographical barrier has isolated the species across its range.
Populations isolated by habitat fragmentation could experience an overall reduction in
genetic exchange between population patches, resulting in differentiation and sub-
structuring of populations (Frankham et al. 2002). Recent landscape changes due to
cattle grazing, agricultural clearing and open cut mining could have contributed to the
contemporary differentiation of the Black-throated Finch populations. Such changes are
less severe in Far North Queensland and Cape York Peninsula than other parts of
Queensland. Birds in Townsville have been exposed to the most severe land clearing
and habitat loss due to pastoralism, agricultural and urban developments. As a result,
much habitats for the Black-throated Finch has disappeared for the Townsville
population, hence more prominent genetic differentiation within the region comparing
with other populations (as shown in our genetic structure analysis).
59
The haplotype network is poorly resolved due to a low level of mitochondrial DNA
sequence polymorphism. All populations shared one haplotype (Hap_BA) with no
distinct separation between populations, which is contrary to the genetic structure
observed in microsatellite DNA. Discrepancies in patterns of population structure from
mitochondrial and microsatellite DNA are not unusual (Johnson et al. 2003, Caparroz et
al. 2009, Hefti-Gautschi et al. 2009, Wenzel et al. 2012). Where weaker mitochondrial
structure is observed, female-biased dispersal is often present. Many bird species have
female-biased dispersal and nuclear DNA based markers often have higher
introgression rates in these species, e.g. Ficedula flycatchers (Saetre et al. 2003);
Hippolais warblers (Secondi et al. 2003); and Amazonian manakins (Brumfield et al.
2001). Although no studies have yet examined the dispersal patterns of the Black-
throated Finch, a high level of female-biased dispersal is unlikely in these birds, because
field observations and banding data showed that both sexes have relatively small home
ranges and the longest travel distance recorded was within 30km, suggesting a restricted
dispersal (Forshaw et al. 2012).
Implications for conservation management
Currently, only the southern subspecies of the Black-throated Finch is recognised by the
Australian Government as of conservation concern. However, I found that genetic
diversity is similarly modest in both northern and southern subspecies. Although there is
no direct evidence of reduced fitness in current populations, low genetic diversity can
still decrease the capacity of the bird to cope with rapid environmental changes,
increasing long-term extinction risk (Johnson et al. 2009, Frankham 2010b). Given the
evidence that the genetic differentiation is weak, it is possible that quick management
actions can restore habitat and population connectivity within regions with little genetic
60
downside. As a result, I suggest that conservation efforts should support continuous
monitoring to identify any rapid population declines as land use intensification occurs
throughout the species’ range. Conservation management strategies that prioritise
increased habitat suitability and restoration of habitat connectivity are encouraged.
61
CHAPTER V
Effects of landscape features on the fine-scale genetic structure of the
endangered Black-throated Finch (Poephila cincta cincta) in
northeastern Australia
(Photo credit: L. Stanley Tang)
62
ABSTRACT
Landscape features and heterogeneity are often reflected in a species’ population
genetic structure. In particular, habitat loss and fragmentation associated with landscape
change often contribute to population decline and may result in changes to population
structure and loss of genetic diversity of remaining populations. To understand the
association between local landscape features and the population structure of a declining
species, I examined the spatial genetic structure of 134 individuals of the threatened
Black-throated Finch (Southern) (Poephila cincta cincta) in north-eastern Australia
using 13 polymorphic microsatellite markers. I tested for isolation by distance and
examined the relationship between the genetic differentiation and current landscape
features by exploring the influences of landscape variables using ecological niche
modelling (ENM) and least cost-path (LCP) analysis. I found strong spatial genetic
structure among sampled individuals, shaped by an open water source (the Ross River
Dam). ENM identified that elevation (41.1%), vegetation structure (21.3%) and water-
related indices (28.1%) contributed the most to the distribution model. Geographic
distance, and LCP cost distance were correlated to a lesser extent with the genetic
distance. My results suggest that the current local landscape features and spatial
separation have a combined effect on the fine scale genetic structure of the Black-
throated Finch in the sampled area. Conservation management strategies that take into
account habitat, water resources and maintaining dispersal corridors are likely to be
effective for the persistence of the species in this region.
63
INTRODUCTION
Identifying relationships between heterogeneous landscapes and genetic structure
Lack of habitat connectivity can significantly impact wildlife populations by restricting
dispersal and creating small isolated populations (Crooks and Sanjayan 2006). Species
inhabiting landscapes fragmented due to human impacts often occur in small isolated
populations, which can be more vulnerable to increased inbreeding depression,
decreased genetic diversity and loss of adaptive potential (Keller and Waller 2002).
Small isolated populations are also more susceptible to demographic and environmental
stochasticity (Hebblewhite et al. 2010). For example, patches of the breeding habitat
(forests) of the endangered Golden-cheeked Warbler (Dendroica chrysoparia) were
cleared for agricultural purposes in central Texas in the USA. Measures of genetic
differentiation (genetic chord distance) were positively correlated with the percentage of
agricultural lands, indicating that birds were not dispersing between patches of breeding
habitats (Lindsay et al. 2008). Similarly, topographical differences between islands
could be the major factor for shaping the strong genetic structure among populations of
endangered species (e.g. Igawa et al. 2013). Water barriers can also reduce gene flow
among mainland and island bird populations, providing evidence of the role of
landscape features in generating genetic variations among populations (Wilson et al.
2011).
The relative importance and influence of landscape features on genetic structure can be
evaluated by landscape genetic approaches. These approaches examine the interactions
between environmental and evolutionary processes such as gene flow, genetic drift and
selection (Manel and Holderegger 2013). The key distinction between landscape
genetics and traditional population genetics is the inclusion of explicit tests of the
64
effects of spatial heterogeneity of landscapes on estimates of gene flow (Holderegger
and Wagner 2008). One of the best approaches is to examine the correlation between
genetic distance and cost distance (estimated from theoretical cost surfaces for
movements between locations) to establish the effects of multiple environmental
variables using geographic information systems (GIS) (Kierepka and Latch 2015).
Many recent studies have demonstrated that this approach is a significant improvement
over traditional methodologies (for example simple isolation-by-distance analysis) for
explaining variation in genetic structure among populations. For example, incorporating
landscape variables substantially increased the fit of the dispersal model (from an r2 of
0.3 to 0.8) explaining genetic differentiation of the Blotched Tiger Salamander
(Ambystoma tigrinum melanostictum) compared with the simple isolation-by-distance
(IBD) approach (Spear et al. 2005). The genetic distance among populations of the
Pacific Jumping Mouse (Zapus trinotatus) was better explained by movement distances
along habitat pathways than using the simple Euclidean distance, demonstrating the
power of considering landscape features in genetic structuring analysis (Vignieri 2005).
Contemporary landscape genetics usually analyses genetic differentiation within and
among spatially separated groups. Sometimes, genetic populations or groups can be
difficult to define, particularly when the species is distributed continuously over the
landscape without clear population boundaries (Kierepka and Latch 2015). In these
situations, clustering methods are usually used to define genetically distinct groups.
However, patterns of spatial genetic structure such as isolation-by-distance can interfere
with estimates of genetic clusters, producing inaccurate results (Latch et al. 2006). An
alternative approach to avoid the difficulty in defining populations is to use individual-
based analytical techniques, because this approach does not require a prior definition of
populations. Instead, individual-based analysis group individuals based solely on the
65
genetic similarity and as a result can offer a means to investigate a wide range of fine-
scale influences of ecological processes on gene flow. For example, an individual-based
approach was used to assess the effects of landscape connectivity (the wooded habitat)
on the dispersal of the European Roe Deer (Capreolus capreolus), because dispersing
deer, especially yearlings, are not confined to discrete populations (Coulon et al. 2004).
Abundance, effective population size and genetic differentiation of the Red-cockaded
Woodpecker (Picoides borealis) were also linked to landscape heterogeneity (patch size
and patch isolation) using an individual-based, spatially explicit population model
which simulated population dynamics and the spatial genetic variation within
heterogeneous landscapes (Bruggeman et al. 2010).
Impacts of changes in the landscape on the survival of granivorous birds in
Australia
In Australia, habitat alteration and landscape changes since European Settlement have
resulted in the decline of a number of amphibian, reptile, bird and mammal species,
including the endangered Black-throated Finch (Poephila cincta cincta) (Morton 1990,
Recher and Lim 1990). In particular, land modification for cattle grazing and
agricultural development as well as changes in frequency and timing of fire have led to
declines of granivorous birds in tropical and subtropical savannahs of northern
Australia, despite the low density of human settlement (State of the Environment
Advisory Council 1996, Franklin et al. 2005).
The movement patterns of the Black-throated Finch are poorly known. In the
Townsville region, recent sighting records and research on the population around the
Ross River Dam identified that the Black-throated Finch could move up to 20km
(Rechetelo 2016). Other observations suggest that the bird may undertake some local
66
movements in response to changes in food availability as well as post-breeding
dispersal (Baldwin 1976, Mitchell 1996, Natural Resource Assessment Environmental
Consultants 2007). Therefore, it is likely that spatial genetic structures exist in
populations of the Black-throated Finch and are associated with landscape features.
Understanding such associations is important for the development and implementation
of appropriate management strategies.
The Ross River Dam was constructed in 1971 for the purposes of water storage and
flood mitigation in the Townsville region. The reservoir has a catchment area of 750km2
and is now considered to be an ecologically important wetland area for Australia
(Australian Nature Conservation Agency, 1996). Land use within the area has created a
heterogeneous landscape that is ideal for spatial genetic analyses because the current
landscape encompasses a wide range of features such as elevated areas (Mt Stuart to the
north, Harvey Range to the west and Mt Elliot to east), cattle grazing areas, riparian
zones and Eucalyptus woodlands.
In this chapter, I evaluate the influences of current landscape features on the spatial
genetic structure of the Black-throated Finch. Specifically, I build a landscape resistance
model based on selected habitat indicators and test this model against the spatial genetic
structure using individual-based approaches within the population that persists around
the Ross River Dam, south of Townsville in north-eastern Queensland, Australia. I
hypothesise that: (1) the heterogeneous landscape in the sampling region has resulted in
a distinct spatial genetic structure for the Black-throated Finch; (2) landscape features
limiting the dispersal of the bird, such as presence of water, vegetation structure and
topography are likely to be associated with patterns of the observed spatial genetic
structure.
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METHODS
Field sampling and genetic procedures
Study area and site selection
On the basis of sighting records in the past five years, I sampled at seven sites where the
Black-throated Finch had been seen regularly around the Ross River Dam, in the
Townsville region, Queensland, Australia (Table V-1). These sites were on the west and
east sides of the dam. The north and south sides of the dam were occupied by urban
developments and inaccessible private properties where birds were either not seen or
impossible to sample. Distances between any two sites ranged from less than 1km to
18km (Figure V-1).
Table V-1 Sampling efforts of the Black-throated Finch (Poephila cincta cincta) in Townsville, Queensland Australia.
Sampling Sites Directions of Ross River Dam
Sampling year Sample size
CLW West 2011-2013 23 LAP West 2009* 48 DOD Southeast 2012 8 FOD East 2012-2013 41 MAD East 2012 4 ANC East 2011-2012 5 ARC Northeast 2013 5 TOTAL 134 * Samples collected and provided by Kim Maute
Sample collection
I collected blood samples from each individual trapped using mist nets following strict
protocols. I sampled a total of 86 individuals from seven sites around the Ross River
Dam between 2011 and 2013. An additional 48 samples were obtained in 2009 from a
previous study (Maute 2011). Details of the sampling procedure are in Chapter II.
68
DNA extraction and Microsatellite genotyping
DNA was extracted from blood spots on FTA© cards and blood cells using ISOLATE
Genomic DNA and ISOLATE II Blood DNA kits (Bioline, Australia), respectively. I
modified the standard protocols recommended by the manufacturer to yield large
amounts (typically between 5-10 μg) of purified DNA. Refer to Chapter II for details on
the DNA extraction protocols. All individuals were genotyped at 18 microsatellite loci
developed specifically for the Black-throated Finch (Tang et al. 2014). Details see
Chapter II.
Figure V-1 Sampling locations (black dots) of the Black-throated Finch (Poephila cincta cincta) around the Ross River Dam in the Townsville region, Queensland
69
Statistical Analysis
Marker characteristics
Microsatellite genotype data were checked for evidence of typographic and scoring
errors (large allele dropout, stuttering and the presence of null alleles) using
MICROCHECKER version 2.2.3 (Van Oosterhout et al. 2004). I also performed the
Ewens-Watterson test in POPGENE version 1.31 (Yeh and Boyle 1997) for neutrality
of all microsatellite markers.
I checked all microsatellite loci for departures from Hardy-Weinberg equilibrium
(HWE) by performing global χ2 goodness-of-fit tests across population-specific FIS
(inbreeding coefficient) estimates and linkage disequilibrium (LD) between all pairs of
loci in GENEPOP version 4.3 (Rousset 2008). I used Markov Chain Monte Carlo
(MCMC) algorithms to estimate the probabilities of all tests with 10,000
dememorisation steps, 1,000 batches and 5,000 iterations per batch (Guo and Thompson
1992, Slatkin and Excoffier 1996). A sequential Bonferroni correction was also applied
to all probability values of multiple comparisons to reduce Type I statistical error (Zar
1999).
Genetic structure
I analysed the population genetic structure and individual admixture based on
microsatellite variation using the Bayesian clustering approach implemented in
STRUCTURE 2.3.4 (Pritchard et al. 2000). This programme uses an MCMC algorithm
to infer distinct genetic clusters (k), estimate allele frequencies in each cluster and
population memberships of each individual. I chose the admixture model, assuming
allele frequencies are correlated among populations, given the geographic extent of
birds sampled in this study. Trial runs without locations as prior produced weak genetic
70
differentiation with no distinct clusters. Therefore, I used an individual-based approach
with sampling locations as prior. The simulations were run for 10 replicates at each k
value ranging from 1 to 10. I chose the upper limit of k to be greater than the number of
sampled locations to account for any distinct genetic clusters that might be present
within each population. Each replicate consisted of a burn-in period of 100,000 MCMC
steps, followed by 2 × 105 iterations. Trial runs indicated the run durations were
sufficient for likelihood values to stabilise. The best supported k values were determined
by posterior probabilities (P(D)) and Δk method (Evanno et al. 2005) in STRUCTURE
HARVESTER version 0.6.94 (Earl and vonHoldt 2012). The clusters output of the
independent runs of the best supported k values were permuted and aligned using the
“Full Search” algorithm to minimise the effects of “label switching” and
“multimodality” in CLUMPP version 1.1.2 (Jakobsson and Rosenberg 2007). The
output files were then visualised in DISTRUCT (Rosenberg 2004).
Landscape genetics
To investigate the influence of environmental variables on population genetic structure
at a local scale, I employed environmental niche modelling (ENM) and the least cost
path (LCP) analysis. ENM predicts the niche of a species using computer algorithms to
represent its known distribution with habitat suitability values on a map with the
integration of complex climatic and landscape variables (Elith and Leathwick 2009).
The LCP is a function implemented in geographic information system (GIS) to
determine the shortest path between geographical locations on the cost surface. It can be
applied to map out likely dispersal corridors of a target species with appropriate
movement cost assignment (Hirzel et al. 2006). By comparing genetic divergence
among individuals between cost distances, I could test hypotheses on the effects of
landscape features and other environmental variables on gene flow. Specifically, I
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conducted the landscape genetic analysis in the following steps: (1) inference of
expected habitat suitability from combinations of environmental variables (listed in the
following paragraph) by ENM; (2) calculation of LCP distance matrix by inversing
habitat suitability values; (3) analyses of correlations between the genetic distance, the
Euclidean distance and the cost distance.
First, I performed ENM analysis in MAXENT version 3.3.3 (Phillips et al. 2006). I
selected eight environmental variables that were reasonably expected to influence
habitat suitability and movement of the Black-throated Finch: the regional ecosystems
(RE), the ground cover disturbance index (GCDI), the normalised difference vegetation
index (NDVI), the normalised difference water index (NDWI), the topographic wetness
index (TWI), the normalised difference infrared index (NDII), slope and elevation.
These variable layers were then trimmed to an area that encompassed all sampling
locations (19.30-19.7171°S and 146.58-147.25°E) and resampled at the same spatial
resolution of 0.0001 decimal degrees (~21m2) because MAXENT requires the same
resolution layers for ENM analysis. I used nearest neighbour assignment method for the
resampling of RE and cubic convolution method for the rest of the variables.
The RE describes vegetation communities and structures that are consistently associated
with a particular combination of topography within a bioregion (Sattler and Williams
1999). I used the original remnant regional ecosystems map (at a scale of 1: 100,000)
from the Queensland Spatial Catalogue of Australia. Individual REs were categorised
separately in the raster layer. The GCDI uses ground cover time series statistics, derived
from the State-wide Landcover and Tree Study (SLATS) Landsat TM imagery over
Queensland, to analyse the percentage ground cover within each regional ecosystem.
This is used as an indication of the intensity of grazing and levels of disturbance. The
72
GCDI data package was acquired from the department of Environment and Heritage
Protection, Queensland Government. I calibrated the corresponding GCDI within each
RE as a grid raster for ENM analysis. The NDVI measures the greenness in an image
and it is often used to monitor the overall biomass and to predict vegetation production
(Lillesand et al. 2004). I prepared the NDVI layer within ArcMap version 10.2 using the
raster calculator on Landsat 8 imagery provided by the US Geological Survey (USGS)
Earth Explorer, based on the equation: 𝑁𝐷𝑉𝐼 = 100×𝐼𝑅−𝑅
𝐼𝑅+𝑅+ 100, where IR is the
pixel values from the infrared band and R is the pixel values from the red band
(Lillesand et al. 2004). The NDWI uses green and near-infrared bands to visualise the
presence of water bodies and the NDII measures the vegetation moisture condition. I
prepared both the NDWI and NDII layers in the same manner as the NDVI, but with
different equations: 𝑁𝐷𝑊𝐼 =𝐺−𝑁𝐼𝑅
𝐺+𝑁𝐼𝑅 (McFeeters 1996) and 𝑁𝐷𝐼𝐼 =
𝑁𝐼𝑅−𝑆𝑊𝐼𝑅
𝑁𝐼𝑅+𝑆𝑊𝐼𝑅 (Ji et
al. 2011), where G, NIR and SWIR are the pixel values from the green, near-infrared and
short-wave infrared bands respectively. The resolution of all bands acquired from
Landsat 8 imagery is 30m. Elevation and slope data were compiled using the global
digital elevation model (DEM) version 2 database (30 metre resolution) from the USGS
Earth Explorer (Hjerdt et al. 2004). The TWI preparation followed the same manner as
elevation and slope, using the equation: ln 𝐹𝐴
tan 𝑏, where FA is the flow accumulation
calculated by estimating water coverage on the DEM in ArcGIS version 10.2 (Wilson
and Gallant 2000).
To construct the environmental niche model, a total of 44 unique occurrence records of
the Black-throated Finch in the study area were used. The occurrence records of the
Black-throated Finch within the study area between 2009 and 2014 included annual
waterhole counts, targeted surveys as well as incidental sightings. Each individual
73
record was checked for accuracy and reliability based on the photographic evidence or
confirmation by a member of the Black-throated Finch Recovery Team. Sightings that
were reported multiple times at the same location were only counted as one record for
the analysis.
I employed the leave-one-out jackknife approach to assess the statistical significance
and to quantify measures of performance of ENMs using the default settings in
MAXENT with 10 replicates. The jackknife test runs the model once with all variables,
dropping out each variable in turn, and then with a single variable at a time (Peterson et
al. 2007, Peterson et al. 2011). I reported the omission rate (OR) based on minimum
training presence threshold and the averaged area under the curve (AUC) of the receiver
operator characteristics (ROC) to measure the overall performance of the suitability
model (Shcheglovitovaa and Andersona 2013). Values of the habitat suitability ranged
from 0 (low) to 1 (high).
Second, I inverted the estimated suitability values to create a friction raster representing
the cost of movement of the Black-throated Finch for each pixel on the map (Hirzel et
al. 2006). I manually modified conspicuous barriers (the Ross River dam and urban
built-up areas) to dispersal as high cost. Location-based pairwise LCP distances and
corridors were estimated between all sampling locations in ArcGIS SDMtoobox version
1.1 (Brown 2014). I manually created the individual-based LCP distance matrix by
assigning the same cost values to individuals that were collected from the same area.
Last, I examined the relationship between three different distance matrices (genetic,
Euclidean and LCP cost distances between any two sampling locations) using Mantel
tests. Specifically, I used the simple Mantel test to investigate the correlation between:
(1) the genetic and the Euclidean distance matrices to detect possible patterns of IBD;
74
(2) the genetic and LCP cost distance matrices to detect possible patterns of isolation by
environmental resistance; and (3) the LCP cost and Euclidean distance matrices to test
whether these two variables are correlated. Individuals collected in the same sampling
point were assigned using a random set of coordinates within a 20-metre radius of the
sampling point. This is to ensure each sample has a set of unique GPS coordinates, and
that does not fall into another grid cell on the map. The individual pairwise chord
distance (Dc) was calculated in FSTAT version 2.9.3.2 (Goudet 1995) and used to
construct the individual pairwise genetic distance matrix because Dc emphasises genetic
drift over mutation, reflects population declines better than other indices, and thus may
be particularly suitable for microsatellite data and fine-scale landscape genetic analysis
(Kalinowski 2002). Then, I used the partial Mantel test to evaluate the correlation
between the genetic and the LCP cost distance matrices alone after accounting for the
possible effects of the IBD. Both Mantel and partial Mantel tests are implemented in
PASSaGE version 2 (Rosenberg and Anderson 2011). Correlation significance was
determined through 10,000 random permutations and Monte Carlo probability values
were calculated.
RESULTS
Marker characteristics
There were no scoring errors associated with stuttering, large allele dropout or null
alleles at any of the 18 microsatellite loci. Marker btfi43 and btfi36 were monomorphic
and marker btfi23 contained 1base pair out-of-phase alleles. As a result, they were not
used in further analysis. After Bonferroni corrections (corrected α = 0.0031), HO
deviations from the HWE were detected at locus btfi27. As a result, it was excluded
75
from further analysis. Linkage disequilibrium of each pair of the remaining 13 loci was
not detected within or among populations after Bonferroni corrections (corrected α =
0.0005).
Genetic structure
The individual based Bayesian clustering analysis in STRUCTURE and the maximum
posterior probability ln P(D) analysis identified three distinct genetic clusters (k = 3,
Figure V-2) with sampling locations as priori. Roughly, birds to the west of the Ross
River dam belonged to one cluster (sites CLW and LAP), birds from the east of the dam
comprised a second admixed cluster (sites FOD, MAD, ANC and ARC) and the
remaining individuals from site DOD were well admixed.
Landscape genetics
Of the 44 occurrence records, 38 were randomly selected by MAXENT for training to
build the model and 6 were used to test the model. The minimum presence threshold is
considered to be a conservative rule for determining whether an evaluation record falls
into or out of the predicted area when calculating the omission rate (OR) (DeLong et al.
1988). The predicted model had an OR of 0 indicating the absence of over fitting. The
Figure V-2 Individual membership coefficients derived from Bayesian inference of the genetic structure of the Black-throated Finch population in the Townsville region, using admixture model (inferred genetic cluster k = 3) with sampling locations as prior. Each cluster is represented as a different colour and a single vertical line represents an individual
76
area under the curve (AUC) value of the model is 0.96 (±0.011 variance), also
indicating a good fit to the species distribution. The estimated suitability map was
congruent with known distribution areas of the Black-throated Finch (Figure V-3).
Analysis of variable contributions showed that elevation, RE, NDWI and TWI
contributed more than 80% to the overall model with elevation the highest (41.1%). The
other three variables contributed much less to the model with GCDI contributing the
least (0.1%) (Table V-2). The least-cost path (LCP) analysis identified potential
dispersal pathways along west and east sides of the Ross River Dam between sampling
locations (Figure V-4).
Table V-2 The relative contributions and jackknife test results of environmental variables to the MAXENT model. RE: regional ecosystems; GCDI: ground cover disturbance index; TWI: topographic wetness index; NDWI: normalised difference water index; NDVI: normalised difference vegetation index; NDII: normalised difference infrared index
Variables Contribution Train Gaina Train Gainb Test Gaina Test Gainb AUCa AUCb
RE 21.28% 0.520 1.169 0.554 1.767 0.792 0.971 GCDI 0.109% 0.003 1.236 0.007 1.642 0.520 0.953 TWI 10.76% 0.246 1.181 0.479 1.480 0.831 0.945 NDWI 17.33% 0.274 1.241 0.064 1.727 0.551 0.961 NDVI 1.927% 0.306 1.205 0.164 1.631 0.632 0.952 NDII 6.775% 0.210 1.099 0.323 1.515 0.670 0.939 Elevation 41.07% 0.778 0.857 1.198 0.962 0.916 0.871 Slope 0.762% 0.197 1.235 0.112 1.711 0.621 0.960 a jackknife test run with only the selected variable b jackknife test run without the selected variable
In both simple Mantel tests, the genetic distance matrix was significantly, but weakly
correlated with Euclidean (Table V-3) and LCP cost distance matrices (R = 0.081, P <
0.001 and R = 0.083, P < 0.001, respectively). Both Euclidean and LCP cost distance
matrices explained less than 1% of the total variation in the genetic distance (R2 =
0.0065 and 0.0067 respectively). Because Euclidean and LCP distances were strongly
correlated (R = 0.982, P = 0.001), I used the partial Mantel test to control the effect of
77
IBD. By keeping Euclidean distance matrix constant, the LCP distance matrix did not
have a significant correlation with the genetic distance (R = -0.003, P = 0.475).
Table V-3 The Euclidean distance (in km, above the diagonal) and the least cost path (LCP) distance (below the diagonal) between each pair of sampling locations
CLW LAP DOD FOD MAD ANC ARCCLW 0.073 18.008 15.919 15.469 15.039 8.984 LAP 0.001 18.054 15.987 15.538 15.112 9.048 DOD 0.216 0.216 8.642 9.674 14.947 18.312 FOD 0.204 0.204 0.095 1.135 6.541 12.086 MAD 0.202 0.202 0.106 0.011 5.413 11.125 ANC 0.253 0.253 0.166 0.066 0.055 7.837 ARC 0.130 0.130 0.240 0.144 0.133 0.086
Figure V-3 Habitat suitability of the Black-throated Finch around the Ross River Dam in Townsville, Queensland, Australia. Clear white circles represent sighting records between 2009 and 2014. White dots with black outlines show sampling locations of this study
78
DISCUSSION
To examine the influence of local landscape features on the genetic structure of the
threatened Black-throated Finch, I first analysed the population genetic structure of
more than 100 individuals from seven locations around the Ross River Dam in the
Townsville region. Then, the environmental niche modelling (ENM) and the least cost
path (LCP) analysis were used to to estimate the habitat suitability. My results provided
evidence that large bodies of open water, i.e. the Ross River Dam, reduced gene flow
among individuals within the area. I also demonstrated landscape variables, such as the
vegetation structure are potential factors driving the fine-scale (within 20km) genetic
Figure V-4 Identified least-cost pathways (yellow lines) from PATHMATRIX simulations, representing potential dispersal corridors of the Black-throated Finch among sampling locations (white dots) in the Townsville region. Thickness indicates the cost values – the thicker the line, the lower the cost of dispersal
79
structure of the Black-throated Finch population around the Ross River Dam. This work
provides essential information for the identification of potential habitat barriers for the
bird. It also aids in developing appropriate habitat management strategies to maintain
high levels of genetic connectivity of the Black-throated Finch.
Spatial genetic structure and isolation by distance (IBD)
The Bayesian clustering analysis based on microsatellite markers revealed varied levels
of genetic separation between groups of birds sampled on west and east sides of the
Ross River Dam that are less than 20km apart. Field observations and banding records
show that the Black-throated Finch in the area rarely move further than 18km. This
means that the population is more likely to be isolated as a result of habitat
fragmentation. This demonstrates that distinct spatial population structuring within the
Black-throated Finch population around the Ross River Dam in the Townsville region
can occur at a scale of 10-20km.
Birds may be expected to experience lower levels of landscape resistance because the
ability to fly allows them to cross potential geographical gaps more rapidly and to cover
larger distances more efficiently than other terrestrial vertebrates. However, population
genetic structures at fine spatial scales have been detected. For example, significant
correlations between genetic and geographic distances were detected within populations
of the Song Sparrow at distance classes less than 10km in North America (Wilson et al.
2011). Similarly, fine spatial scale (less than 200m) genetic structures were found in
other passerines such as the White-breasted Thrasher (Ramphocinclus brachyurus)
(Temple et al. 2006) and the Superb Fairywren (Malurus cyaneus) (Double et al. 2005).
80
Water barriers have been shown to limit movement in many avian taxa over short
distances. For example, the lower portion of the Amazon River is wider and more open;
hence it is a more effective barrier to dispersal and gene flow than other parts of the
river for many forest-restricted species, particularly the antbirds (Thamnophilidae)
(Hayes and Sewlal 2004). It is evident that the presence of the dam acted as a barrier to
gene flow within the population of the Black-throated Finch around the Ross River Dam
near Townsville. Field observations suggest that the Black-throated Finch prefers to
drink water from waterholes, small farm dams surrounded by trees and farm troughs or
water tanks (Immelmann 1982, Natural Resource Assessment Environmental
Consultants 2007, Forshaw et al. 2012). Large bodies of water (e.g. the Ross River
Dam) are less frequently visited by the bird probably due to the high risk of predation or
to the high energy cost of crossing over as demonstrated in many small passerines (e.g.
Brawn et al. 1996, Adams and Burg 2015).
Although weak, the IBD structuring found in our analysis suggests that the geographical
distance that the Ross River Dam created also restricts the gene flow of the Black-
throated Finch within the area. Variable IBD patterns can be observed across a species’
range depending on the spatial scale examined (Hutchison and Templeton 1999, Castric
and Bernatchez 2003, Garnier et al. 2004). Studies have suggested that many factors,
including localised differences in migration, time since populations have been present in
a region and local landscape heterogeneity can influence IBD patterns (Coulon et al.
2004, Bradbury and Bentzen 2007). The Ross River Dam was constructed in 1971 for
the purposes of flood mitigation and water storage for the Townsville region. Within the
45 years since the dam was constructed, the current genetic differentiation within the
population developed and clustering analyses and shows it was associated with the
geographical distance created by the dam. However, it is possible that such a level of
81
differentiation is not significant enough to support a strong IBD pattern. The weak IBD
pattern may also be because the spatial intervals between sampled locations were
relatively far apart and a finer gradient of distances between sampling locations is
needed for a stronger IBD pattern (Smouse and Peakall 1999). Finally, other local
landscape features such as mountain ranges, vegetation structure and presence of small
water bodies within the region also had a stronger influence than spatial distance in the
study area (as discussed below).
Influences of landscape variables
Based on the jackknife test of variable importance, I identified that elevation and
vegetation structure (RE) are important contributors to the distribution of the Black-
throated Finch in the Townsville area. Water related indices (NDWI and TWI) also
contributed significantly to the distribution model of the Black-throated Finch. Strong
contributions from both RE and water-related indices are also consistent with the results
from field surveys and previous studies that evaluated habitat requirements of
Australian grassfinches (Jennings and Edwards 2005, Maute 2011, Forshaw et al. 2012).
Their results showed that the Black-throated Finch is often found in the vicinity of
water, and mainly associated with specific vegetation structure such as grassy, open
woodlands and forests (Black-throated Finch Recovery Team 2007).
The association between the distribution of Black-throated Finch and the landscape
variables (identified above) around the Ross River could be the result of balanced
energy intake and cost of foraging and breeding. The Black-throated Finch prefers to
forage on open ground (e.g. roadsides, and small clearings in the understorey)
surrounded by trees or shrubs to which they can fly when disturbed or alarmed
(Immelmann 1982, Natural Resource Assessment Environmental Consultants 2007,
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Rechetelo 2016). Such foraging behaviour suggests that the bird requires a clear line of
sight to detect both aerial and ground predators. Dense ground cover with tall vegetation
or elevated ground surface may reduce the ability to detect danger; hence increase the
cost of foraging (Tang and Schwarzkopf 2013). The daily requirement of water also
determines that the Black-throated Finch inhabits areas in the vicinity of water bodies,
e.g. waterholes, small dams, creeks and water troughs. Dry and elevated areas, such as
Mount Stuart (north of the Ross River Dam) and Harvey Range (west of the Ross River
Dam), have higher evaporation rates and faster runoff due to shallow soils and exposed
grounds (McCain 2009). As a result, the water availability is much lower than it is at
lower altitudes.
Mantel tests revealed that the observed genetic distance matrix was weakly correlated
with the LCP distance matrix. Although less than 1% of the variation in genetic
distances could be explained by the LCP cost distance calculated from the ENM, I
detected a significant association between landscape features and the genetic structure at
a spatial scale of less than 20km. However, the relationship disappeared when the
geographic structure common to both matrices was accounted for (partial Mantel R = -
0.003, P = 0.475). The strong correlation between the LCP cost and geographic
distances showed that the explanatory power of the LCP cost is probably due to the
spatial patterns of the LCP cost distance. Therefore, landscape features alone do not
explain the spatial genetic structure of the Black-throated Finch in the Townsville
region.
Recent geographical fragmentation within a population may not necessarily result in a
significant genetic differentiation between sub-populations, because the allele frequency
differences may take a long time to build up and become fixed. For example, it would
83
take 2.77Ne (effective population size) generations for an allele with an initial frequency
of 0.5 to become fixed (Kimura and Ohta 1969). My Bayesian clustering analysis based
on the admixture model showed that many individuals within each cluster had mixed
origins indicating some level of gene flow still exists across the Ross River Dam in the
Townsville region. This is also supported by the LCP analysis, in which I identified
multiple possible dispersal corridors (Figure V-3) for gene flow. Substantial local
landscape changes occurred only 30 – 50 years ago, a weak association between the
genetic structure and other landscape features is expected. Nonetheless, by sampling
individuals from seven sites that span 0.07-18km around the Ross River Dam in the
Townsville region, I was able to show that the combination of environmental and
geographical factors (such as landscape features, the presence of Ross River Dam, and
the geographical distance) is likely to reflect in the spatial genetic structure observed.
Implications for conservation management
The inferences associated with the fine-scale spatial genetic structure have significance
for the conservation of the Black-throated Finch. Understanding the scale at which gene
flow predominates can indicate the approximate scale of demographic independence
(e.g. Diniz and Telles 2002).
Dispersal over small spatial scales is important to maintain genetic connectivity
(Sunnucks and Taylor 2008) and this work highlights the relationship between
landscape features (such as open water, mountain ranges, long geographic distance,
unsuitable vegetation structure and absence of small water bodies) and dispersal
limitation in the Black-throated Finch. Although a strong spatial genetic structure was
detected, there was some level of gene flow between sub-populations around the Ross
River Dam. Therefore, managing the Townsville population of the Black-throated Finch
84
as a whole with conservation strategies focusing on retention of corridors and suitable
habitat would be a priority to ensure species persistence in the region. It is also
important to conduct on-going genetic monitoring to maintain genetic diversity of the
population.
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CHAPTER VI
Conservation values and genetic diversity retention from wild and
captive populations of a threatened species, the Black-throated Finch
(Poephila cincta)
(Photo credit: L. Stanley Tang)
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ABSTRACT
Ex situ conservation programmes and reintroductions of captive bred animals are a
frequent component of endangered species conservation strategies. However, captive
populations may go through repeated bottlenecks, leading to a lack of genetic diversity
and to genetic differentiation between various captive and wild populations. In this
chapter, I examine the genetic diversity of 96 captive individuals of the Black-throated
Finch acquired from various aviculturists in Australia, using 14 polymorphic
microsatellite markers. I also compare the genetic diversity (measured as
heterozygosity, HO and allele richness, r), effective population size (Ne), inbreeding (F)
and relatedness (Rr) between captive and wild populations. I find that all captive
populations have a lower level of genetic diversity (average HO = 0.35 among captive
populations versus average HO = 0.45 in the wild; average r = 2.34 in captivity versus
3.08 in the wild); smaller effective population sizes compared with all wild populations;
higher levels of inbreeding (F = 0.114 in the wild versus F = 0.216 in captivity) and
similar levels of individual relatedness. Individual assignment tests identify mixed
origins of captive birds, the majority of which cannot be traced back to current wild
populations that were sampled in this project. My results suggest that the Black-throated
Finch in captivity has lost genetic variability to some degree and increased levels of
inbreeding may potentially reduce its viability. However, the presence of captive birds
that originated from wild populations that are now believed to be extinct makes these
individuals particularly important. These findings allow us to recommend that in situ
conservation strategies should be the priority and if captive breeding programs are
considered, birds of known origins should be kept and bred separately.
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INTRODUCTION
In situ and ex situ conservation
In situ and ex situ strategies are distinct approaches for the conservation of wildlife
(Pritchard et al. 2011). In situ conservation emphasises the protection of threatened
species in their natural habitats, with strategies usually focused on the improvement or
protection of suitable habitats and ecosystems through the establishment of national
parks, nature reserves and other conservation areas, as well as the removal of
threatening processes (Pritchard et al. 2011). Ex situ conservation, on the other hand,
focuses on the preservation of endangered species in human controlled environments,
such as zoos, aquaria, botanical gardens, seed banks and captive animal breeding
centres. Such strategies are usually the last resort to protect an endangered species
because of the organism’s inability to survive in the wild without human intervention
(Witzenberger and Hochkirch 2011).
In situ conservation preserves biodiversity, which includes both declining species and
the evolutionary processes that enable them to adapt to the changing environment. Such
evolutionary processes enable species to develop potentially important and useful
genetic traits in response to environmental stochasticity (Frankham et al. 2002).
Although in situ conservation represents the preferred way and arguably the most
effective means of conserving threatened and endangered species, viable populations of
some species can only be maintained ex situ (Pelletier et al. 2009). For example, the
endangered Asian Crested Ibis (Nipponia nippon) persisted as only two pairs
rediscovered in 1981. Without captive breeding, the species would have an extremely
high probability of extinction due to the loss of genetic diversity and elevated
vulnerability to environmental catastrophes (Zhang et al. 2004). Currently, more than 60
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species are found only in captivity (animals) or under cultivation (plants) (BirdLife
International 2012) and about 3,000 species are likely to require ex situ breeding to
prevent extinction in the next 200 years (Seal 1991, Magin et al. 1994).
Despite the increasing demand for ex situ conservation programmes, a number of
challenges must be overcome in order to increase the likelihood of survival of captive
populations and the success of reintroduction programmes. First, it is important to
minimise the inbreeding depression and the accumulation of deleterious alleles. Captive
populations of many species originate from a small number of founders as a result of
difficulties in field collections or simply because there are no more in the wild (Leberg
and Firmin 2008). For example, the last wild California condor (Gymnogyps
californianus) was brought into captivity in 1987 and the surviving population to date
are all descendants of the last 14 wild birds. The severe population bottleneck has led to
an increased frequency of a lethal allele for chondrodystrophy (Ralls and Ballou 2004).
Second, genetically distinct populations in the wild should ideally be represented in the
captive population to maximise genetic variability, and so that the ability to adapt to
different conditions can be retained. This is particularly important for species that are
patchily distributed and/or show strong genetic structuring among existing populations.
For example, founders of the captive Jamaican Yellow Boa (Epicrates subflavus)
originated from one wild population even though more wild populations exist.
Decreased genetic variability was clearly identified by the fact that the captive
population had fewer alleles compared with the wild populations (Tzika et al. 2009).
Furthermore, the genetic adaptation to captivity should be minimised, because captive
bred individuals frequently have reduced fitness when reintroduced to the wild resulting
in lowered reintroduction success of captive-bred individuals compared with
translocations of wild individuals (Williams and Hoffman 2009).
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In situ conservation is considered to be the legal and institutional priority for many
conservation organisations and agreements, such as the Convention on Biological
Diversity (Pritchard et al. 2011). Ex situ strategies can however be a useful complement
to in situ measures, because they directly preserve the target species against extinction
and provide valuable resources which may aid in the recovery of endangered species.
However, many ex situ populations are not established until the species has gone
through major declines and there are few surviving individuals in the wild (Wilson et al.
2012). Consequently, there is limited retention of original genetic diversity. The
International Union for Conservation of Nature (IUCN) recommends that a detailed
review of the species should be carried out before ex situ conservation programmes are
established. Such reviews ideally include all factors related to the life history,
taxonomy, current population status, demographic and genetic viability as well as
current threats to species persistence (IUCN/SSC 2014).
Conservation of the Black-throated Finch
In Australia, habitat alteration and landscape changes have resulted in the decline of
many faunal groups, including granivorous birds, and appropriate conservation
management strategies are needed (Lindenmayer 2009). In particular, the tropical and
subtropical savannahs of northern Australia, which support the largest granivorous bird
assemblages, have experienced the greatest declines (State of the Environment Advisory
Council 1996, Franklin et al. 2005).
The Black-throated Finch (Poephila cincta) in particular has suffered a substantial
decline as a result of habitat modification and loss. The current conservation efforts are
in situ, such as the acquisition of population information (e.g. population size, structure
and abundance), understanding the ecology and identifying the relative importance of
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key threats (Mitchell 1996, Black-throated Finch Recovery Team 2007, Maute 2011,
Rechetelo 2016).
The Black-throated Finch is generally common in Australian aviculture. However, it is
less popular than other related grassfinches such as the Long-tailed Finch (Poephila
acuticauda) due to certain behavioural traits (such as nest destructiveness and
aggressiveness towards other finches) and colour variations (Forshaw et al. 2012). The
total number of the Black-throated Finch held by aviculturists in Australia was
estimated to be approximately 593 in 2011 (Fitt and Pace 2011).
Colour mutations and hybridisation with other finch species have also been observed
among aviary birds. Roughly 15% of the total number of the captive Black-throated
Finch are identified as colour mutants (Fitt and Pace 2011). The high mutation rate
among captive birds raises concerns for the future viability of the species in captivity,
and may also indicate the presence of potential negative genetic processes such as
bottlenecks and inbreeding.
To evaluate the potential value of captive populations in species persistence for a
threatened granivorous bird, I assessed the potential genetic conservation values of the
Black-throated Finch held by aviculturists in Australia. In particular, I compare the
genetic diversity of groups of captive and wild populations of birds; estimate the
effective population sizes; and test captive birds for genetic bottleneck, relatedness and
inbreeding depression. I hypothesise that (1) through the process of domestication, birds
in captivity have lowered genetic diversity compared with those in the wild; (2)
signatures of genetic bottlenecks are present as a result of high levels of inbreeding in
captivity; (3) individuals in captivity are more related than those in the wild.
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METHODS
Field sampling and genetic procedures
Sample collection
I sampled wild birds from four locations across the current range where the Black-
throated Finch had been seen regularly since 2009. The southern form (P. c. cincta) was
sampled in the Townsville region as well as in the Desert Uplands in central
Queensland. The northern form (P. c. atropygialis) was sampled at Mareeba in far north
Queensland. I collected small amounts (30-50μL) of blood from each individual trapped
using mist nets following strict protocols. Blood samples were applied to FTA© card
and dried for further analysis. Samples for Lakefield National Park on Cape York
Peninsula were obtained from a previous study (Maute 2011) and stored as blood cells
in 70% ethanol. All wild samples were collected between 2009 and 2013. Detailed
sampling protocols are in Chapter II.
I sourced samples of captive birds from Australian aviculturists, who keep records of
each individual bird they possess, in the states of Queensland and New South Wales. I
only assigned the location information to those birds, of which owners had records of
the origin. Birds that had no origin information were included in one group (see Table
II-1 in Chapter II). Each finch owner collected blood samples from their own birds
following the same FTA© card protocol as mentioned above.
DNA extraction and microsatellite genotyping
DNA was extracted from blood spots on FTA© cards and blood cells using ISOLATE
Genomic DNA and ISOLATE II Blood DNA kits (Bioline, Australia), respectively. I
modified standard protocols as recommended by the manufacturer to yield large
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amounts (5-10μg) of purified DNA. Refer to Chapter II for details on the DNA
extraction protocols. All individuals were genotyped at 18 polymorphic microsatellite
loci developed specifically for the Black-throated Finch (Tang et al. 2014). Details see
Chapter II.
Statistical Analysis
Marker characteristics
All microsatellite genotype data were checked for evidence of typographic and scoring
errors (large allele dropout, stuttering and the presence of null alleles) for both wild and
captive populations of the Black-throated Finch using MICROCHECKER version 2.2.3
(Van Oosterhout et al. 2004).
I analysed all microsatellite loci to test for departures from Hardy-Weinberg equilibria
(HWE) within each population by performing global χ2 goodness-of-fit tests across
population-specific FIS (inbreeding coefficient) estimates, and linkage disequilibrium
(LD) between all pairs of loci in GENEPOP version 4.3 (Rousset 2008). I used Markov
Chain Monte Carlo (MCMC) algorithms to estimate the probabilities of all tests with
10,000 dememorisation steps, 1,000 batches and 5,000 iterations per batch (Guo and
Thompson 1992, Slatkin and Excoffier 1996). A sequential Bonferroni correction was
also applied to all probability values of multiple comparisons to reduce Type I statistical
error (Zar 1999).
Genetic variability
In order to compare the genetic diversity between wild and captive groups of the Black-
throated Finch, I calculated the observed heterozygosity (HO), the unbiased expected
heterozygosity (HE) and the number of private alleles (NPA) using GenAlEx version 6.5
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(Peakall and Smouse 2006, 2012). The average allelic richness (number of alleles, r)
and allele frequencies were calculated in FSTAT version 2.9.3.2 (Goudet 1995). The
method used to calculate r incorporates a rarefaction method to compensate for unequal
sample sizes (el Mousadik and Petit 1996), as is appropriate given the sampling design
employed. For captive birds, I treated individuals that were of the same origin as a
single population. There were only two individuals from NSW, insufficient to form a
separate population. I grouped these two individuals with SEQ because SEQ is
geographically adjacent to NSW. I also compared the genetic differentiation between
each pair of populations using the unbiased Nei’s genetic distance (D) and tested the
significance of population differentiation by analyses of molecular variance (AMOVA)
using allele frequencies in ARLEQUIN version 3.5.1.3 (Excoffier and Lischer 2010). I
used all captive populations as one group and all wild populations as a second group so
that the differentiation among and within populations and groups could be compared.
The significance of the analysis was tested running 10,000 permutations.
I used two standard ad hoc methods to test for signatures of recent genetic bottleneck
within the captive population. First, heterozygosity excess was tested assuming a two-
phase mutation (TPM) model with 95% stepwise mutations, 5% multiple-step
mutations, and a variance among multiple steps of 12, in BOTTLENECK 1.2.02 (Piry et
al. 1999). The TPM model is considered to be the most appropriate for microsatellite
data (Piry et al. 1999). Significance of heterozygosity excess over all loci was
determined with a one-tailed Wilcoxon sign rank test after 10,000 iterations as
implemented in the program. Second, I used the M-ratio method developed by Garza
and Williamson (2001) to calculate the ratio (M) of the total number of alleles to the
range in allele sizes, and its critical value MC (5% of values fall below MC as
determined by simulations). M and MC were estimated using M_P_VAL and
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CRITICAL_M, respectively (Garza and Williamson 2001). I assigned three required
parameters that were required for both programmes with recommended values: (1) pre-
bottleneck θ = 10; (2) the percentage of one step mutations, ρs = 0.9; and (3) the mean
size of non one-step mutations, Δg = 3.5. Each set of simulations consisted of 10,000
iterations.
Effective population sizes
To measure the ability of populations to maintain genetic variation and to characterise
the risk status, I estimated the effective population sizes (Ne) for all wild and captive
populations using a combination of three methodologies. This is to increase the
reliability of the estimation and is due to the fact that Ne estimates vary depending on
the influencing factors that each method is based on (Waples and Do 2010). First, I
estimated Ne based on linkage disequilibrium taking into account the effects of genetic
drift on allele frequencies (Hill 1981) with bias correction (Waples and Do 2010).
Second, I calculated Ne from molecular coancestry, considering the level of allele
sharing within each population (Nomura 2008). Both linkage disequilibrium and
molecular coancestry methods are implemented in NeEstimator version 2.01 (Peel et al.
2004). Last, I used a Bayesian approach, assuming a continuous Brownian motion
model for microsatellite data, to estimate the mutation-scaled effective population size
Θ = 4Neμ (where μ is the mutation rate per site per generation, assumed constant for all
loci) implemented in MIGRATE-n version 3.6.8 (Beerli 2009). The Bayesian inference
in this programme accounts for the influence of gene flow on the within-population
genetic diversity (Beerli and Felsenstein 2001).
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Genetic structure
I compared the population genetic structure and individual admixture between wild and
captive populations based on microsatellite variation using the Bayesian clustering
approach implemented in STRUCTURE 2.3.4 (Pritchard et al. 2000). This programme
uses an MCMC algorithm to infer distinct genetic clusters (k), estimate allele
frequencies in each cluster and population memberships of each individual. I chose an
admixture model, assuming allele frequencies are correlated among populations. First, I
used the sampling locations as prior for wild populations and considered all captive
individuals as one population. Second, I assessed the population structure of the captive
birds separately using their origins as prior. Birds that were of unknown origins were
grouped together. Trial runs without locations as prior produced weak genetic
differentiation with no distinct clusters. The simulations were run for 10 replicates at
each k value ranging from 1 to 10. I chose the upper limit of k to be greater than the
number of assigned populations to account for any sub-structuring. Each replicate
consisted of a burn-in period of 100,000 MCMC steps, followed by 2 × 105 iterations.
Trial runs indicated the run durations were sufficient for likelihood values to stabilise.
The best supported k values were determined by posterior probabilities (P(D)) and Δk
method (Evanno et al. 2005) in STRUCTURE HARVESTER version 0.6.94 (Earl and
vonHoldt 2012). The clusters output of the independent runs of the best supported k
values were permuted and aligned using the “Greedy Search” algorithm to minimise the
effects of “label switching” and “multimodality” in CLUMPP version 1.1.2 (Jakobsson
and Rosenberg 2007). Outputs were then visualised as a cluster plot in DISTRUCT
(Rosenberg 2004).
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I also performed assignment tests in GenAlEx to genetically assign individuals of the
captive population to their population origin in the wild. For captive birds of known
origin, I compare the assignment results with the reported origins so the extent of
genetic exchange within a captive population can be identified. For individuals of
unknown origin, I identified the most likely source populations. I used the “leave one
out” option in the software to account for potential bias. Specifically, this method
removes the genotype of the test individual from the population it was sampled in
before estimating the expected allele frequencies (Efron 1983).
Genetic relatedness and inbreeding
In order to estimate the genetic similarity between each pair of individuals in both wild
and captive populations, I calculated the pairwise relatedness values based on a
maximum likelihood method that uses the genotypes of a triad of individuals (trioML).
This method reduces the chance of misidentifying genes identical in state as identical-
by-descent and allows for inbreeding as well as genotype errors in data (Wang 2007).
The simulated mean relatedness values were then compared to the theoretical
relationship classification (0.5 for parent-offspring and full siblings, 0.25 for half
siblings and 0 for unrelated) and 95% and 99% confidence intervals were obtained by
bootstrapping with 1,000 repeats. I then estimated the individual inbreeding coefficient
using the same statistical approach. Both analyses were implemented in
COANCESTRY version 1.0.1.5 (Wang 2010). Last, I compared the averaged
relatedness and inbreeding coefficient between wild and captive populations in
COANCESTRY using the same bootstrapping procedure to estimate the confidence
intervals. All captive populations were combined as a single group for both relatedness
and inbreeding analyses to increase the sampling size and also because many captive
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birds are known to be exchanged between breeders and pooling them is appropriate for
identifying potential genetic relatedness between individuals.
RESULTS
Marker characteristics
There was no evidence for scoring errors associated with stuttering, large allele dropout
or null alleles at any of the 18 microsatellite loci scored. Neutrality tests confirmed that
all of the microsatellite and mitochondrial DNA markers were neutral. Markers btfi43
and btfi36 were monomorphic and marker btfi23 contained 1base pair of out-of-phase
alleles. Therefore, they were not used in further analysis. After Bonferroni corrections
(corrected α = 0.0031), HO deviations from the Hardy-Weinberg Equilibrium (HWE)
were detected in at least one population at 5 loci (btfi04, btfi27, btfi31-2, btfi40 and
btfi41). However, only locus btfi27 showed consistent deviation across all populations
and was excluded from further analysis. Linkage disequilibria of each pair of the
remaining 14 loci were not detected within or among populations after Bonferroni
corrections (corrected α = 0.0005). This set of markers showed a modest level of
polymorphism over both wild and captive populations of the Black-throated Finch. The
number of alleles ranged from 2 (btfi36) to 14 (btfi05) with an average of 4.5 (± 0.365
standard error) alleles per locus.
Genetic diversity and structure
The number of private alleles was highest in the TSV population (NPA = 4), followed by
the CEQ population (NPA = 3). Apart from FNQ, all wild populations had private alleles.
Among the captive populations, private alleles were present only in SEQ (NPA = 2) and
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UNK (NPA = 1). The lowest observed heterozygosity was also detected in one of the
captive populations (AYR) with an average of 0.274 (± 0.057 standard error). The
averaged observed heterozygosity among wild populations was significantly higher than
it was among captive populations (P = 0.027 < 0.05, 1,000 permutations). The allele
richness showed a moderate level of variation within each population (ranged from 2.07
to 3.24). The averaged r among captive populations was significantly smaller than that
among wild populations (P = 0.022 < 0.05, 1,000 permutations) (Table VI-1).
Table VI-1 Measurements of genetic diversity and results of genetic bottleneck analysis for each of the Black-throated Finch (Poephila cincta) populations. NPA: number of private alleles; HO: observed heterozygosity; HE: expected heterozygosity; r: average allelic richness. M: ratio of total number of alleles to the range of allele sizes; MC: critical value of M; PW: probability value of one-tailed Wilcoxon sign rank test for heterozygosity excess. For M-ratio method, if M < MC; and for Heterozygosity excess method, if PW < 0.05, a recent bottleneck is likely to be present
Region NPA HE (SE) HO (SE) r (SE) M MC PW
SEQ 1 0.43±0.07 0.39±0.07 2.41±0.24 0.541 0.677 0.339 AYR 0 0.41±0.08 0.27±0.06 2.07±0.32 0.899 0.556 0.813 ROC 0 0.35±0.09 0.34±0.09 2.28±0.20 0.839 0.66 0.271 UNK 2 0.43±0.07 0.39±0.07 2.61±0.33 0.887 0.717 0.449 CEQ 3 0.43±0.08 0.40±0.08 3.00±0.45 TSV 4 0.45±0.08 0.44±0.09 3.13±0.47 FNQ 0 0.48±0.07 0.47±0.07 2.96±0.43 CYP 2 0.50±0.08 0.48±0.08 3.24±0.48
Estimates of the population genetic differentiation, Nei’s unbiased genetic distance D is
strongly correlated with allele richness (Pearson’s correlation, R = 0.768, P = 0.026 <
0.05). In general, as populations diverge, the genetic diversity (measured as allele
richness r) decreases. All captive populations are more genetically distinct from the
wild populations; and there is a higher level of genetic diversity within wild populations
than captive ones (Figure VI-1). AMOVA results showed that two groups (wild and
captive) were significantly different (FCT = 0.010, P = 0.025 < 0.05). The difference
between populations within groups (FSC = 0.061, P < 0.01) and within populations (FST
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= 0.071, P < 0.01) was also significant. In all cases, most of the genetic variation (93%)
was observed within populations (Table VI-2).
Table VI-2 Analysis of molecular variance (AMOVA) grouping populations of the Black-throated Finch according to sources (wild and captive)
Source of variance
df Sum of squares
Variance components
Percentage of variation
Between sources (wild and captive)
1 33.135 0.034 1.04 (FCT = 0.010, p = 0.025)
Between populations within sources
6 103.624 0.202 6.07 (FSC = 0.061, p < 0.001)
Within populations
668 2061.259 3.086 92.89 (FST = 0.054, p < 0.001)
Total 675 2198.018 3.322 100
There was no evidence for a significant genetic bottleneck detected in any of the captive
populations using the heterozygosity excess method. However, using M-ratio method,
the SEQ population showed a significant signal of genetic bottleneck (M-ratio 0.541 <
MC = 0.677). When all captive birds were considered as one population, both methods
failed to detect signals of significant genetic bottleneck (Table VI-1).
The individual based Bayesian clustering analysis in STRUCTURE identified two
distinct genetic clusters (k = 2) taking into account all wild and captive populations
(Figure VI-2). Notably, all wild populations belonged to one cluster and the captive
population formed another, showing the differentiation between wild and captive birds.
When considering the captive population alone, I identified 8 genetic clusters (k = 8).
Birds that had known origins were roughly grouped into four clusters, mirroring their
geographic origins. Birds that had unknown origins were approximately grouped into
another four clusters with some individuals assigned to the same clusters of the birds
that had known origins (Figure VI-2).
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Further population assignment tests showed that 75 out of 96 captive birds were self-
assigned (78%). Out of the 21 individuals that were assigned to other populations, 13
were to CEQ population, 5 to the TSV population and 3 to FNQ. Only 7 out of the 49
captive birds of unknown origin were successfully assigned to wild populations with 3
to TSV population and 2 each to CEQ and FNQ populations (Table VI-2).
Effective population size (Ne), inbreeding and relatedness
Estimates of Ne varied widely depending on the methods used for each population.
However, the Ne of the captive population was consistently smaller than the observed
population size (N = 96): Ne = 10.1 using the linkage disequilibrium method; and Ne =
3.3 using the molecular coancestry method. The mutation-scaled effective population
size showed a similar trend: it is the smallest in the AYR population (Θ = 0.001) and
highest in the TSV population (Θ = 0.096). The averaged Θ is also significantly smaller
Figure VI-1 The genetic distance in relation to allele richness among captive (grey triangles) and wild (black squares) populations
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among captive populations (0.012) than that of the wild (0.071) (two-sample t test, t (1), 6
= 2.79, p = 0.016 < 0.05) (Table VI-3).
Table VI-3 Comparison of estimates of effective population sizes (Ne) in each population using three different methods. Θ: mutation-scaled effective population size; *: captive populations
Region Linkage disequilibrium Molecular coancestry Bayesian Ne 95% CI Ne 95% CI Θ 95% CI SEQ* 2.4 1.7-3.6 3.2 2.0-4.7 0.019 0.014-0.021 AYR* 1.0 0.8-1.5 1.7 1.2-2.4 0.001 0.000-0.002 ROC* 7.4 2.9-17.4 3.5 2.1-5.2 0.009 0.002-0.011 UNK* 11.4 7.4-17.0 8.3 3.1-16.2 0.019 0.002-0.021 CEQ inf 173.6-inf inf inf-inf 0.088 0.075-0.095 TSV 75.3 52.5-115.2 9.6 2-23.1 0.096 0.093-0.100 FNQ 13.5 6.9-34.5 14.1 0.4-52 0.009 0.007-0.012 CYP 117.9 49-inf 28 4.6-71.8 0.091 0.085-0.097
The average inbreeding coefficient was significantly higher within the captive
population (F = 0.216) compared with that in any of the wild populations (between
0.099 and 0.130). The population pairwise comparisons showed no significant
differences in the level of inbreeding between all wild populations. All statistical
significance was determined by 95% and 99% confidence intervals (Table VI-4).
The averaged relatedness estimate was slightly higher among captive birds (Rr = 0.176)
than wild populations (Rr = 0.145). The individual relatedness within population is
greatest in FNQ with an average of 0.184, followed by the captive population (Rr =
0.176). However, they were not significantly different (estimate of the difference fell
within 95% confidence intervals). Similarly, the level of individual relatedness was not
significantly different between CYP and CEQ populations. The individual relatedness
between populations showed that FNQ and CYP are more related that any other
populations (Rr = 0.131 and all other Rr values were less than 0.1). In both the captive
and FNQ populations, 40-50% of the dyads (pairs of individuals) are distantly related
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(Rr < 0.1) and about 8% are closely related; whereas in other wild populations, more
than 50% of the dyads were distantly related and less than 2% were closely related
(Figure VI-3).
Table VI-4 Pairwise comparisons of the inbreeding coefficient (F) between and within each Black-throated Finch population. Numbers along the diagonal are F values within each population and variances are summarised in brackets. Numbers below the diagonal are F values between each population pair. * indicates significant difference with 95% confidence interval and ** indicates significant difference with 99% confidence interval
TSV CEQ FNQ CYP CAP TSV 0.099 (0.014) CEQ 0.031 0.130 (0.020) FNQ 0.022 -0.009 0.121 (0.019) CYP 0.009 0.022 0.013 0.108 (0.012) CAP 0.117 ** 0.085 ** 0.095* 0.108 ** 0.216 (0.043)
Figure VI-2 Comparison of the individual relatedness among wild (CEQ, CYP, FNQ and TSV) and captive (CAP) populations. Each bar represents the percentage of dyads in the relevant population
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Figure VI-3 Individual membership coefficients derived from Bayesian inference of genetic structure across all captive and wild Black-throated Finch populations using admixture model (top, k = 2); and among all captive populations using admixture model (bottom, k = 8). A single vertical line represents an individual. Each colour represents a genetic cluster
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DISCUSSION
It is essential to evaluate and compare the genetic diversity and viability of both the in
situ and ex situ populations before any appropriate conservation strategies can be
inferred. I examined conservation values of four major wild populations of the Black-
throated Finch with 96 captive individuals using microsatellite markers. It is evident
that the genetic diversity is moderately higher in wild populations than it is among
sampled captive birds from various sources. Wild and captive populations of the finch
are also genetically differentiated. Wild birds showed lower levels of inbreeding and
individual relatedness than the captive population. This work provides genetic
information useful for developing ex situ conservation strategies of the species.
Significant differentiation between captive and wild populations
Using variable microsatellite markers, I was able to detect significant differences
between captive and wild populations of the Black-throated Finch. The genetic diversity
measured as allele richness and observed heterozygosity, was significantly higher in
wild populations than in captive ones. This is probably due to genetic drift in captivity
and/or the larger effective population size in the wild. Typically, genetic drift occurs in
small populations, where rare alleles are more likely to be lost. The result of this process
reduced genetic diversity and increased genetic differentiation (Herdrick 2011). Wild
populations of the Black-throated Finch showed modest levels of genetic differentiation
across its current distribution (Chapter IV) and the estimates of Ne were significantly
larger than captive populations demonstrated by multiple methods. Therefore, it is
possible that the high number of alleles per locus that exist in the wild cannot be
maintained in captivity as a result of genetic drift, leading to the observed reduction in
allele richness and heterozygosity. The significant genetic differentiation between the
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wild and captive Black-throated Finch was also supported by the population pairwise
comparisons and AMOVA (the overall higher values of Nei’s unbiased genetic distance
D among captive populations than wild ones and the significant FRT value).
Genetic differentiation between captive and wild populations is common in many avian
species. For example, the Zebra Finch, one of the most common aviary birds in the
world, showed significant differentiation between wild and captive populations (FST =
0.062). There was also a reduction in the genetic diversity (captive r = 11.7 compared to
wild r = 19.3) among captive populations across the world (Forstmeier et al. 2007).
The increased genetic differentiation is particularly apparent in threatened species,
highlighting the importance of understanding these genetic characteristics when
designing effective conservation strategies. For example, current captive stocks of the
endangered White-headed Duck (Oxyura leucocephala) have suffered a significant loss
of genetic diversity due to founder effects and/or genetic drift and were not suitable for
reintroduction. As a result, the development of a more diverse captive population based
on birds taken from different areas of the range has been strongly recommended
(Munoz-Fuentes et al. 2008). Overall, the observed higher level of genetic diversity in
wild Black-throated Finch populations indicates that the evolutionary potential of the
wild birds is still greater than that of the captive ones, as expected.
Effective population sizes and inbreeding
For both direct estimates of the effective population size, in all captive populations of
the Black-throated Finch and the combined captive populations, the Ne was less than 12.
Ne, however, was significantly larger for wild populations than for those in any of the
captive ones. Small Ne increases the risk of inbreeding. The classic “50/500”
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conservation guideline states that a population with an Ne < 50 is vulnerable to the
immediate effects of inbreeding depression with high risk of extinction; and a
population with an Ne < 500 is vulnerable to extinction in the long term (Franklin 1980).
However, it is widely believed that populations must be higher than implied by the
“50/500 rule” in order to survive and maintain genetic diversity (Lande 1995, Reed and
Bryant 2000). It is therefore evident that the effective population sizes of the captive
Black-throated Finch are too small to maintain high levels of genetic diversity. This is
also supported by the fact that the level of inbreeding observed among captive birds (F
= 0.216) is almost double that among wild birds (F ranged between 0.099 and 0.130).
Although the actual cost of inbreeding in populations of animals remains largely
unknown, it is suggested that when the level of inbreeding is above intermediate levels
(F = 0.3 – 0.4), there is an increased probability of extinction (Frankham 1995). The
inbreeding coefficients of both wild and captive populations have not reached this
threshold, indicating that the viability of all populations of the Black-throated Finch is
unlikely to have been significantly affected by inbreeding. However, it is evident that
inbreeding depresses components of reproductive fitness in a population. This is
particularly the case in captive animals. For example, inbreeding has depressed the
fitness of virtually every species of livestock in fertility, birth weight, growth rate,
disease resistance and productivity (Lacy 1993).
I did not measure the individual fitness of the Black-throated Finch to quantify the
potential impact of increased inbreeding among captive populations due to limited
resources and logistic constrains. However, observations of behavioural and
morphological traits among captive birds made by various aviculturists suggest some
level of fitness loss. For example, in captivity, the Black-throated Finch is considered to
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be aggressive. Particularly, male birds were observed to be so restlessly fond of nest
building that they frequently disturb females that are sitting on eggs, leading to females
becoming egg-bound (Butler 1899, Forshaw et al. 2012). This aggression reduces the
breeding success of captive birds, but it has not been observed in the wild.
Morphologically, the Black-throated Finch has several recognised colour mutations
(Kingston 2010). In Australia, birds with mutations represent about 15% of all Black-
throated Finches listed in a national survey of native finches held in captivity in 2011 by
the National Finch and Softbill Association (Fitt and Pace 2011). Such high rates of
mutation are probably due to the increased level of inbreeding and/or selective
hybridisation with other Australian native finch species, e.g. the Long-tailed Finch
(Poephila acuticauda), the Masked Finch (Poephila personata), the Zebra Finch
(Taeniopygia guttata) and the Double-barred Finch (Taeniopygia bichenovii) (Forshaw
et al. 2012). Therefore, I argue that the increased level of inbreeding in conjunction with
decreased fitness has reduced the genetic viability of the Black-throated Finch in
captivity.
Individual assignments and relatedness
Individuals in the captive population are only slightly more related to one another than
are members of wild populations. The pairwise relatedness analysis showed that more
than two thirds of the dyads in each population had relatedness values less than 0.2,
indicating that a majority of the individuals in both captivity and the wild are not closely
related. This suggests that a captive breeding programme for reintroduction could
source individuals from among captive populations. The advantage of using existing
captive birds is that these individuals have already undergone some degree of
domestication, which potentially reduces the stress level of birds and increases their
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productivity. However, the current captive populations of the Black-throated Finch
demonstrated reduced genetic diversity and small effective population sizes, increasing
the chance of the loss of behaviour traits that are critical for adaptation to natural
environments (Shepherdson 1994).
Furthermore, I found that only approximately 22% of individuals in captivity were
successfully assigned to any of the major wild populations and over half of these
individuals were assigned to the CEQ populations. The low level of assignment success
to current wild populations may reflect the moderate level of colour mutations found
among captive birds as discussed above. It also suggests that the origins of captive birds
could be from locations other than the geographical regions I sampled for this study.
According to the owners of the sampled captive birds, a large proportion of them were
derived from individuals legally trapped in the early 1900s from Southern Queensland
and northern New South Wales, a population now considered to be extinct in the wild. I
assigned 32 of the 96 captive birds to SEQ, representing a third of the overall captive
populations sampled. Other identified source populations for the captive birds include
the Rockhampton region (ROC) in central Queensland and the Ayr region (AYR) in
north Queensland, just south of Townsville. These wild populations were not strongly
represented in sampled captive birds, but they are important in maintaining the genetic
diversity of the Black-throated Finch in captivity.
Conservation values of captive birds and management implications
Ex situ conservation measures, e.g. captive breeding programmes, are considered to be
complementary to in situ methods because they provide “insurance” against species
extinction. Through reintroduction, they often play a crucial role in preventing critically
endangered species from becoming extinct in the wild, given that functioning wild
109
populations can be established from the captive population. Ex situ strategies are usually
the last resort to preserve the genetic diversity of species that are under drastic decline
in the wild.
Wild populations of the Black-throated Finch have declined significantly in the past 50
years and the current conservation strategies have been largely in situ. Although, they
are relatively common in captivity, my results showed that the genetic diversity was
lower among the captive populations than the wild ones. A higher level of inbreeding
was also detected among captive birds. The genetic differentiation between captive and
wild birds were significant. Given the fact that the genetic diversity of the wild
populations is still moderate and the genetic connectivity is still relatively high, in situ
management strategies (such as maintaining habitat suitability and increasing habitat
connectivity) should be considered a priority.
However, my study has identified potential values of captive birds for ex situ
conservation management. First, I found evidence that birds originated from
populations that are now extinct (southern Queensland population in particular) in the
wild are still maintained in captivity. These particular birds are valuable for the genetic
diversity of captive breeding stocks. Therefore, it is vital to ensure these individuals are
kept and bred separately from other birds so that the distinct genetic lineages are not
lost. Second, the level of relatedness among captive birds is similar to that found in the
wild. Existing management strategies could be applied to manage individuals of the
Black-throated Finch that are closely related or inbred in captivity. For example, the
maximum avoidance of inbreeding (MAI) scheme where family sizes are equalised and
females are mated to males of different subpopulations each year (Kimura and Crow
1963, Frankham et al. 2002); and the rotational breeding scheme where breeding circles
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are established by providing males from each subpopulation for its neighbouring
subpopulation (Windig and Kaal 2008). Third, I demonstrated that molecular data could
be used to identify individual relatedness of the Black-throated Finch. In combination
with the knowledge of birds with known origins, it is possible to establish detailed
individual pedigree information using the genetic markers described in this study.
If a captive breeding programme for the purpose of wild reintroductions was going to be
established in the future, it is important to work with captive breeders to retain genetic
diversity of the declining Black-throated Finch. Specifically, I recommend the
following steps: (1) establish a genetic structure of captive birds that are derivatives of
wild populations of the Black-throated Finch including populations that are extinct in
the wild; (2) define management units (populations that have different allele
frequencies, but do not necessarily show fixed differences between them) based on
more genetic data over a broader region and prioritise these units within the species; (3)
assign captive individuals to defined management units following the methods used in
this study; (4) select suitable individuals to establish a breeding stock for each unit; (5)
collect additional wild individuals, if possible, to maximise genetic variability within
each unit; and (6) apply management strategies mentioned above to minimise
inbreeding and reassess genetic structure of the captive population regularly.
112
Understanding the genetic processes of any declining species is vital to assess the
viability of surviving populations. It is also an important step towards the
implementation of the most appropriate conservation management strategies. Genetic
studies of declining granivorous birds in Australia and specifically in the context of the
Black-throated Finch (Poephila cincta) are rarely conducted. My project set out to
examine the genetic viability of a threatened granivorous bird in Australia. Using the
Black-throated Finch as a case study, my project examined key genetic processes
involved in the decline of the bird as a result of habitat alteration in recent years. I also
evaluated the conservation values of captive populations and made recommendations
for the conservation of the Black-throated Finch. In my project, I sought to answer three
key questions:
1. What is the level of genetic diversity and population structure of the Black-
throated Finch across its current range?
2. Is there any association between landscape features and the spatial genetic
structuring of the Black-throated Finch?
3. Are captive populations of the Black-throated Finch valuable for future re-
introduction programmes?
I used both population and landscape genetic approaches with microsatellite and
mitochondrial DNA markers to address these key questions at regional and fine spatial
scales.
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EMPIRICAL FINDINGS
Range-wide genetic structure and diversity
I sampled four populations spanning over 750km in northern Queensland, Australia.
According to sighting records, these four populations represent the main of areas where
the Black-throated Finch currently occurs. In particular, the Townsville region and
central Queensland are the only known strong holds of the surviving southern
subspecies in the wild. Based on 14 microsatellite markers, I found modest levels of
genetic diversity (average allelic richness r = 4.37 ± 0.41 standard error and average
heterozygosity HO = 0.42 ± 0.040 standard error) with no bottleneck signature within
these populations. The level of genetic diversity is similar across all populations at
species level. However, populations of the northern subspecies were slightly more
genetically diverse than those of the southern subspecies.
I also provided molecular evidence that the genetic structure of sampled populations of
the Black-throated Finch are differentiated, particularly between northern and southern
forms. The observed differentiation and sub-structuring of populations is likely due to
the isolation caused by habitat fragmentation and as a result of restricted dispersal (see
detailed discussion in Chapter IV).
Effects of landscape features
Radio tracking and banding data suggest that the Black-throated Finch does not move
further than 30km (Rechetelo 2016). Changes in local environment may have a bigger
impact on the survival of the species compared with more mobile species. The
Townsville region has gone through intense land modifications for cattle grazing,
agricultural and urban developments in the past 20 years. Sighting records of the Black-
114
throated Finch suggest that populations in the Townsville region have declined
significantly and are fragmented as a result of habitat changes. Therefore, it is necessary
to understand the association between the population genetic structure and the
heterogeneous landscape that the bird lives in.
I sampled birds from sites that spanned no more than 20km between the two most
distant sampling points. I found that local landscape features, including the presence of
water bodies, vegetation structure and terrain are correlated with the genetic distance
between individuals. I provided evidence that genetic structuring can be detected at fine
scales for species that have limited dispersal ability. Although weak, I also
demonstrated the effects of open waters on the spatial genetic structure of the Black-
throated Finch in northeastern Queensland. In this case, the Ross River Dam has a
surface area of 82km2; it is likely to have acted, to some extent, as a barrier of gene flow
separating birds into two genetic groups. The Ross River Dam was built only about 40
years ago for the purpose of flood control and water supply to the Townsville region
and the presence of genetic differentiation was not overwhelmingly significant.
Nonetheless, it demonstrated that the genetic structure of the Black-throated Finch
could be influenced by local landscape features and highlighted the potential for open
water bodies to act as barriers to the gene flow.
Conservation values of ex situ populations
I evaluated the genetic diversity and structuring of captive individuals of the Black-
throated Finch sourced from various breeding stocks held by aviculturists in Australia.
The captive populations showed lowered levels of genetic diversity compared with wild
populations. Although no evidence of high levels of inbreeding was detected, I found
that the effective population sizes were smaller and individuals were more related to
115
each other in captive populations than they were in the wild. The genetic differentiation
was also significant between captive and wild populations.
Due to the significant differentiation between captive and wild populations of the
Black-throated Finch, I recommended that conservation efforts should focus mainly on
in situ populations at present. However, based on the fact that the inbreeding depression
was not detected in captivity and the genetic distinctiveness of many captive
individuals, it is valuable to use captive individuals and to work with breeders to retain
genetic diversity of the captive population (see Chapter VII for detailed discussion).
IMPLICATIONS FOR CONSERVATION MANAGEMENT
Using multiple genetic approaches, my thesis has some critical implications for the
conservation management of the Black-throated Finch. First, I recommend that in situ
conservation strategies should be given priority. This is based on the fact that current
major populations of the Black-throated Finch have not yet experienced severe genetic
bottlenecks and the level of genetic diversity is similarly moderate across all
populations. Specifically, the conservation effort should focus on the following areas.
a) Continuous monitoring of population trends to allow quick detection of significant
decline. Currently, there are only limited regular surveys conducted each year to
monitor the Black-throated Finch. The on-going annual waterhole counts has been
the major source of population estimates in the Townsville region (Black-throated
Finch Recovery Team 2007). Other intensive field surveys and monitoring work
have been conducted by environmental consulting companies for various
development projects in both Central and North Queensland (Natural Resource
Assessment Environmental Consultants 2005, 2007, GHD Australia 2010, 2012).
116
Information collected from these surveys is nonetheless crucial, but a more
intensive, regular and systematic survey programme should be used to monitor
population trends in multiple locations, particularly the Townsville region and
Central Queensland – the two known remaining strong-holds of the southern
subspecies.
b) Increasing habitat suitability and connectivity My landscape genetic analysis
indicated that large open water bodies and areas with dense understorey may reduce
gene flow of the Black-throated Finch. Maintaining areas with critical water
sources, particularly in the dry season, and controlling invasive grass species to
create mosaic ground cover with open patches are essential to increase the habitat
suitability and connectivity. It is also important to combine my findings with other
ecological studies (e.g. movements and breeding ecology) to determine more
comprehensive habitat requirements for the Black-throated Finch, so that
appropriate measures can take place for the future land management.
c) Applying landscape genetics analysis. I used the Townsville population to
investigate the association between local landscape features and population genetic
structuring. The landscape genetics technique I used may be applied to other areas
that are of conservation concern. For example, many Black-throated Finch habitats
are threatened by land development, particularly for coal mines (GHD Australia
2012). Landscape genetics analysis could be used to identify potential corridors to
gene flow of the bird. It could also reveal local landscape features that are
associated with genetic structuring. This spatial genetic information could aid in
relevant environmental impact assessments and the conservation of local Black-
throated Finch populations.
117
d) Ex situ conservation strategies. My results demonstrated that captive individuals of
the Black-throated Finch have potential for the establishment of captive breeding
programmes. The genetic diversity was only slightly lower than that of wild
populations. Many individuals in captivity represented lineages originated from
now extinct populations in the wild. Ex situ conservation efforts should be focused
on maintaining the genetic diversity of captive populations; ensuring individuals
originated from unique lineages are kept and bred separately; and minimising
inbreeding. A detailed molecular and genotypic analysis could also be carried out to
build a comprehensive pedigree network, so that viable and healthy individuals can
be selected for the maintenance of breeding stocks.
RECOMMENDATIONS FOR FUTURE RESEARCH
Due to time and funding constraints, as well as the rarity of the Black-throated Finch
and the difficulty in sampling, I was able to collect only a limited number of samples.
Despite providing some crucial evidence and information for the conservation of the
bird, I acknowledge that there are still many areas that require further research in order
to produce a more comprehensive and long-term conservation strategy for the survival
of the Black-throated Finch.
First, I recommend that more populations should be sampled. It is very time-consuming
to discover and establish a reliable site where birds visit frequently and are easy to trap
at the same time. In the future, it is important to survey more potential waterholes and
other possible sites for sample collections. Particularly, the priority should be given to
establish more sampling sites in Central Queensland, where the largest numbers of the
Southern Black-throated Finch are present. In the northern range of the species, more
118
sites representing different geographical locations across the Cape York Peninsula to
Mareeba Wetlands in Far North Queensland would be ideal. In the Townsville region,
for fine-scale genetic analysis, more sampling locations could be established to the
north and southwest of the Ross River Dam, as well as a few sites further away from the
dam, e.g. north of the Townsville city. With more sampling locations included, it would
be easier to construct a more detailed genetic structure and to estimate more accurately
the genetic diversity of the surviving populations of the Black-throated Finch.
Second, I recommend that more genetic markers should be used to increase the
statistical power so that more detailed genetic structuring can be detected. Specifically,
it would be helpful to use large numbers of single nucleotide polymorphisms (SNPs) in
combination with microsatellite and mitochondrial markers to map out an allele
spectrum across the entire genome for each population. This would provide a higher
resolution for detecting subtle population genetic structuring of the Black-throated
Finch. Using universal DNA markers on sex chromosomes can also be important to
directly examine sex-linked characteristics, e.g. female-biased dispersal and parental
analysis.
Third, I recommend sourcing more historical samples from museum collections to
examine temporal changes to determine if the moderate level of genetic diversity was
historically present or due to recent habitat fragmentation. Knowing both the temporal
and spatial patterns of the genetic structure would provide a much more comprehensive
understanding of the changes in genetic diversity of the Black-throated Finch. Samples
collected in captivity could include more individuals from different sources. For
example, birds from wildlife parks and zoos in Australia and overseas; birds with
mutations and different colour morphs; as well as possible interspecific hybrids. It is
119
possible to establish a complete genetic database of the Black-throated Finch in
captivity so that healthy and viable breeding stocks can be maintained and sourced if
captive breeding programmes were required.
Last, I suggest that demographic data, including survival rates of each life stage,
individual fitness, life cycles, clutch sizes, and growth rates, of the Black-throated Finch
should be collected in detail. In combination with the genetic information I have
provided in this thesis, it would then be possible then to perform an robust population
viability analysis, so that the extinction risk could be estimated and the overall future
population performance could be projected.
Overall, this project examined the genetic diversity, population structuring and the
landscape genetics of the Black-throated Finch. Results revealed valuable information
on the current stage of the species both in the wild and in captivity. Based on this
information, I have provided recommendations to the conservation of the Black-
throated Finch in the future.
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Field Protocol: Avian blood collection using FTA® cards
1. Unfold one wing and hold tightly to avoid the bird moving.
2. Expose the venepuncture site by using alcohol (70%) to wet the feathers, then
separate the feathers with fingers; the ulnar vein should be seen as it passes over the
ventral side of the elbow.
3. Pluck some feathers if necessary to improve visibility; plucked feathers should be
placed in ziplock bags with clear labels.
4. Apply light pressure on the vein towards the heart to temporarily block blood flow so
that the vein can be easily located.
5. Carefully use the hypodermic needle (25 or 26 gauge) to prick the vein so that the
blood emerges onto the skin.
6. Use heparinised capillary tube (~70μL in capacity) to carefully absorb blood.
7. Collect 1/3 – 1/2 tube of blood for each bird and transfer the blood to the Classic
FTA® card (Whatman, USA). Blood spots should be dried and absorbed by the card
in shaded areas and placed in paper envelopes for safe and easy storage.
8. Apply pressure to the vein using cotton wool for 30-60 seconds to prevent excessive
bleeding and hematoma formation.
9. Check if the bird is in good condition and if so, release on site; otherwise put the bird
back into the bag for 30 minutes to allow resting before release.
139
Laboratory Protocol: DNA extraction from avian blood stored on FTA©
cards using ISOLATE Genomic DNA Kit (BIOLINE, Australia)
This protocol is modified from the product manual of ISOLATE DNA kits of BIOLINE.
It is designed for isolating genomic DNA from FTA cards/filter papers.
Equipment
• Micro punch, 1.2mm (can be replaced by normal hole punch or fine scissors)
• Smooth fine-point tweezers
• Three beakers filled with diluted bleach, ethanol, and water respectively
• Laboratory-standard tissue paper
• Shaking water bath or oven
• 1.5mL tubes (one per sample)
• Microcentrifuge with rotor for 1.5mL and 2mL tubes
• Two waste collection plates
• Full set of pipettes and autoclaved tips
Solutions
<!> indicates a hazardous substance, further details give below
• Ethanol, 96-100% <!> flammable<!>
• ddH2O
• ISOLATE Genomic DNA kit (components as tabled below)
Regent 10 preps 50 preps 250 preps Lysis Buffer D <!> flammable, irritant<!> 5mL 25mL 120mL Binding Buffer D <!> flammable, irritant<!>
2 × 2mL 15mL 70mL
Proteinase K 0.3mL 1.5mL 5 × 1.5mL
140
Hazards & Safety considerations
Minimum personal protective equipment (PPE)
• Long sleave laboratory coat & latex gloves throughout.
Hazardous Waste Disposal
Wash Buffer D 6mL 24mL 2 × 60mL Elution Buffer 2 × 2mL 25mL 110mL
Hazard (%) Risk Phrases Safety Phrases/Precautions
Lysis Buffer D <!>flammable<!> <!>irritant<!> R11, R36, R67 S7, S16, S24/25, S26
Highly flammable, Irritating to eyes, Vapours may cause drowsiness and dizziness
Keep container tightly closed; Keep away from sources of ignition; Avoid contact with skin and eyes; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice
Binding Buffer D <!>flammable<!> <!>irritant<!> R11, R36, R67 S7, S16, S24/25, S26
Highly flammable, Irritating to eyes, Vapours may cause drowsiness and dizziness
Keep container tightly closed; Keep away from sources of ignition; Avoid contact with skin and eyes; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice
Proteinase K R36/37/38, R42 S22, S24, S26, S36/37
Irritating to eyes, respiratory system and skin; May cause sensitization by inhalation
Do not breathe dust; Avoid contact with skin; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice; Wear suitable protective clothing and gloves
Ethanol, 96% <!>flammable<!> R11 S2, S7, S16
Highly flammable
Keep out of the reach of children; Keep container tightly closed; Keep away from sources of ignition
141
• Dispose of tubes and tips via MEEL approved (ChemWatch Labelled) containers.
• Dispose of all wet waste via dilution with tap water to <20% and dispose of via
fume hood sink flushing for at least 2 minutes (<50mL) or 20 minutes (> 50mL).
Important considerations
• Use sterile equipment only. Be very aware of any possible sources of
contamination- particularly genetic contamination between samples.
• Use a control extraction.
• Carefully label all tubes throughout the extraction.
• Add 96-100% ethanol <!>flammable<!> to the Wash Buffers as indicated on the
bottles, mix thoroughly.
• Reconstitute lyophilised Proteinase K in water, 0.3mL for each 10 preps. Aliquot to
avoid freeze thawing.
• Prepare a 50°C shaking water bath or heating block for Proteinase K lysis cell
membranes.
Method
1. Punch up to 5 small pieces (1.2mm diametre) from the FTA card, place in 1.5mL
tube.
2. Add 200μL Lysis Buffer D and 15μL Proteinase K. Mix by vortexing.
3. Incubate at 50ºC overnight and vortex intermittently to disperse the sample.
4. Centrifuge at 10,000 × g (12,000rpm) for 1 minute. Transfer the supernatant to
another 1.5mL tube.
5. Add 200μL Binding Buffer D to the sample. Mix well by vortexing for 15 seconds.
6. Transfer the sample to Spin Column D placed in a 2mL Collection Tube.
7. Centrifuge at 10,000 × g (12,000rpm) for 2 minutes. Discard the Collection Tube
and place Spin Column D in a new Collection Tube.
(Ensure there is no lysate remaining on Spin Column D. If required, centrifuge Spin
Column again until all liquid has passed through the membrane.)
8. Add 350μL Wash Buffer D.
142
9. Centrifuge at 10,000 × g (12,000rpm) for 1 minute. Discard the filtrate and reuse
the Collection Tube.
10. Repeat steps 8 and 9.
11. Centrifuge at maximum speed for 2 minutes to remove all traces of ethanol.
Discard the Collection Tube.
12. Place Spin Column D into a 1.5mL Elution Tube.
13. Add 50μL Elution Buffer directly to the Spin Column membrane and incubate at
room temperature for 1 minute.
14. Centrifuge at 6,000 × g (8,000rpm) for 1 minute to elute DNA.
(Use a lower volume of Elution Buffer if a high concentration of DNA is required.
Alternatively, perform two elution steps with an equal volume of Elution Buffer to
increase the yield)
(Repeat step 13 – 14 four times until the final Elution Buffer is 200μL)
15. The isolated DNA is ready for use in downstream applications or for storage at -
20°C. For long-term storage, freeze at -70°C.
16. Aliquot DNA into three tubes of 50μL × 2 (working stocks, -20°C) and 100μL
(long term storage, -80°C).
143
Laboratory Protocol: DNA extraction from avian blood cells using
ISOLATE II Blood DNA Kit (BIOLINE, Australia)
This protocol is modified from the product manual of ISOLATE II Blood DNA kits of
BIOLINE. It is designed for isolating genomic DNA from avian blood cells.
Equipment
• Shaking water bath or oven
• 1.5mL tubes (one per sample)
• Microcentrifuge with rotor for 1.5mL and 2mL tubes
• Two waste collection plates
• Full set of pipettes and autoclaved tips
Solutions
<!> indicates a hazardous substance, further details give below
• Ethanol, 96-100% <!>flammable<!>
• ddH2O
• ISOLATE II Blood DNA kit (components as tabled below)
Regent 10 preps 50 preps 250 preps Buffer G1 <!>irritant<!> 3.2mL 10mL 50mL Buffer G2 0.8mL 2.5mL 12.5mL Proteinase K Buffer PR 0.8mL 1.8mL 8mL Proteinase K (lyophilised) 6 mg 30 mg 2 × 75 mg Wash Buffer GW1 <!>flammable, irritant<!>
6mL 30mL 2 × 75mL
Wash Buffer GW2 4mL 7mL 2 × 20mL Elution Buffer G 4mL 13mL 60mL
144
Hazards & Safety considerations
Hazard (%) Risk Phrases Safety Phrases/Precautions
Buffer G1 <!>irritant<!> R11, R36, R67 S7, S16, S24/25, S26
Irritating to eyes, Vapours may cause drowsiness and dizziness
Keep container tightly closed; Avoid contact with skin and eyes; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice
Wash Buffer GW1 <!>flammable<!> <!>irritant<!> R11, R36, R67 S7, S16, S24/25, S26
Highly flammable, Irritating to eyes, Vapours may cause drowsiness and dizziness
Keep container tightly closed; Keep away from sources of ignition; Avoid contact with skin and eyes; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice
Proteinase K R36/37/38, R42 S22, S24, S26, S36/37
Irritating to eyes, respiratory system and skin; May cause sensitization by inhalation
Do not breathe dust; Avoid contact with skin; In case of contact with eyes, rinse immediately with plenty of water and seek medical advice; Wear suitable protective clothing and gloves
Ethanol, 96% <!>flammable<!> R11 S2, S7, S16
Highly flammable
Keep out of the reach of children; Keep container tightly closed; Keep away from sources of ignition
Minimum personal protective equipment (PPE)
• Long sleave laboratory coat & latex gloves throughout.
Hazardous Waste Disposal
• Dispose of tubes and tips via MEEL approved (ChemWatch Labelled) containers.
• Dispose of all wet waste via dilution with tap water to <20% and dispose of via
fume hood sink flushing for at least 2 minutes (<50mL) or 20 minutes (> 50mL).
Important considerations
145
• Use sterile equipment only. Be very aware of any possible sources of
contamination- particularly genetic contamination between samples.
• Use a control extraction.
• Carefully label all tubes throughout the extraction.
• Blood samples should be stored at -20ºC or -70ºC for long-term storage; room
temperature or 4ºC storage is only for short-term usage for up to several days.
• Transfer all contents of Buffer G1 to Buffer G2, mix well and label Buffer G3
• Add 80mL × 2 ethanol (96-100%) to Wash Buffer GW2 concentrate and label
“ethanol added”
• Add 3.35mL × 2 Proteinase K Buffer PR to the lyophilised Proteinase K (brief
centrifuge first before adding buffer)
• Proteinase K solution is stable at -20ºC for 6 months – make one solution at a time
Method
Protocol volumes are for 16 samples
1. Set incubator to 70ºC and preheat Elution Buffer G to 70ºC.
2. Prepare and label 16 × 1.5mL microcentrifuge tubes.
3. Mix 400µl (25µl × 16) Proteinase K solution with 3200µl (200µl × 16) Lysis Buffer
G3
4. Add 225µl mixed solution to each sample.
5. Incubate samples at 70ºC for 1h and vortex vigorously 4 times.
(The lysate should turn brownish during incubation.)
6. Add 210µl ethanol (96-100%) to each sample and vortex.
7. Transfer the sample (all lysate) to a spin column in a collection tube and centrifuge
at 11,000g for 2 minutes. Place column in a new collection tube (2mL).
8. Add 500µl Wash Buffer GW1 and centrifuge at 11,000 × g for 1 minute. Place
column in a new collection tube (2mL).
9. Add 600µl Wash Buffer GW2 and centrifuge at 11,000 × g for 2 minutes. Discard
flow-through and re-use the collection tube.
10. Centrifuge at 11,000 × g for a further 1 minute to remove all ethanol residuals.
Discard flow-through and put the column in a new 1.5mL microcentrifuge tube.
146
11. Add 50µl preheated Elution Butter G (70ºC) directly onto the silica membrane and
incubate at room temperature for 5 minutes.
12. Centrifuge 11,000 × g for 1 minute.
(Repeat step 10-11 to allow high yield & high concentration of DNA.)
147
Laboratory Protocol: PCR product clean-up using MicroCLEAN Kit
(MICROZONE, UK)
Equipment
• Centrifuge or microfuge
• Pipettes and tips
• PCR plates or strip tubes
Solutions
<!> indicates a hazardous substance, further details give below
• MicroCLEAN solution <!> irritant<!>
Hazards & Safety considerations
Hazard (%) Risk Phrases Safety Phrases/Precautions
MicroCLEAN (Microzone Limited) <!> irritant<!>
Harmful if swallowed, Irritant, Irritating to eyes, respiratory system and skin
In case of contact with eyes, rinse immediately with plenty of water for at least 15 min. In case of contact with skin, immediately wash skin with soap and copious amounts of water If inhaled, move to fresh air and monitor breathing. Administer first aid or seek medical advice if breathing is absent or difficult. If swallowed, wash out mouth with water if person is conscious, and seek medical aid.
Minimum personal protective equipment (PPE)
148
• Long sleeve laboratory coat & latex gloves throughout.
Hazardous Waste Disposal
None
Important considerations
• Use sterile equipment only
• Store at 4°C
Method
1. Add equal volume of MicroCLEAN <!> irritant<!> to PRC product.
2. Mix by pipetting at least 10-15 times.
3. Leave at room temperature for 5 minutes.
If using tubes:
1. Spin at high speed (10,000 – 13,000 × g) in microfuge for 7 minutes.
2. Remove supernatant.
3. Spin briefly again and remove dregs.
4. Resuspend pellet in the appropriate amount of dH2O.
5. Leave for 5 minutes to rehydrate DNA.
If using plates:
1. Spin at 3200 × g (4000 rpm in Beckman centrifuge with swing bucket rotor) for 40
minutes.
2. Place plate upside down onto tissue paper in the centrifuge holder.
3. Pulse centrifuge to 200 × g for 2 minutes.
4. Resuspend pellet in the appropriate volume of dH2O.
5. Leave for 5 minutes to rehydrate DNA and vortex to mix.
149
Raw Data I: details of the Black-throated Finch samples acquired from the
wild used in the analysis
Region codes:
TSV: Townsville Coastal Plains, Queensland
CEQ: Central Queensland
FNQ: Far North Queensland
CYP: Cape York Peninsula, Queensland
Site codes:
LAP: Ladham Park, Ross River Dam west
CLW: Clear Water, Ross River Dam west
DOD: Dottrel Dam, Ross River Dam south
FOD: Ford’s Dam, Ross River Dam southeast
MAD: Mango Dam, Ross River Dam southeast
ANC: Antill Creek, Ross River Dam east
ARC: Sunbird Creek, Ross River Dam north
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES kBTFI001 02647271 TSV LAP cincta kBTFI002 02647272 TSV LAP cincta kBTFI003 02647273 TSV LAP cincta kBTFI004 02647274 TSV LAP cinctakBTFI005 02647275 TSV LAP cincta kBTFI006 02647276 TSV LAP cincta kBTFI007 02647277 TSV LAP cincta
‡ Leg ring coding system followed the standards set out by the Australian Bird and Bat Banding Scheme (ABBBS).
150
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES kBTFI008 02647278 TSV LAP cincta kBTFI009 02647279 TSV LAP cincta kBTFI010 02647280 TSV LAP cincta kBTFI011 02647281 TSV LAP cincta kBTFI012 02647282 TSV LAP cincta kBTFI013 02647283 TSV LAP cincta kBTFI014 02647284 TSV LAP cincta kBTFI015 02647285 TSV LAP cincta kBTFI016 02647286 TSV LAP cincta kBTFI017 02647287 TSV LAP cincta kBTFI018 02647288 TSV LAP cincta kBTFI019 02647289 TSV LAP cincta kBTFI020 02647290 TSV LAP cincta kBTFI021 02647291 TSV LAP cincta kBTFI022 02647292 TSV LAP cincta kBTFI023 02647293 TSV LAP cincta kBTFI024 02647294 TSV LAP cincta kBTFI025 02647311 FNQ LFN atropygialis kBTFI026 02647312 FNQ LFN atropygialis kBTFI027 02647316 FNQ LFN atropygialis kBTFI028 02647317 FNQ LFN atropygialis kBTFI029 02647320 FNQ LFN atropygialis kBTFI030 02647321 FNQ LFN atropygialis kBTFI031 02647322 FNQ LFN atropygialis kBTFI032 02647323 FNQ LFN atropygialis kBTFI033 02647324 FNQ LFN atropygialis kBTFI034 02647325 FNQ LFN atropygialis kBTFI035 02647326 FNQ LFN atropygialis kBTFI036 02647327 FNQ LFN atropygialis kBTFI037 02647328 FNQ LFN atropygialis kBTFI038 02647329 FNQ LFN atropygialis kBTFI039 02647330 FNQ LFN atropygialis kBTFI040 02647331 FNQ LFN atropygialis kBTFI041 02647332 FNQ LFN atropygialis kBTFI042 02647333 FNQ LFN atropygialis kBTFI043 02647334 FNQ LFN atropygialis kBTFI044 02647335 FNQ LFN atropygialis kBTFI045 02647336 FNQ LFN atropygialis kBTFI046 02647337 FNQ LFN atropygialis kBTFI047 02647338 FNQ LFN atropygialis kBTFI048 02647339 FNQ LFN atropygialis kBTFI049 02647870 FNQ LFN atropygialis kBTFI050 02647871 FNQ LFN atropygialis kBTFI051 02647872 FNQ LFN atropygialis kBTFI052 02647873 FNQ LFN atropygialis kBTFI053 02647874 FNQ LFN atropygialis kBTFI054 02647875 FNQ LFN atropygialis
151
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES kBTFI055 02647876 FNQ LFN atropygialis kBTFI056 02647877 FNQ LFN atropygialis kBTFI057 02647878 FNQ LFN atropygialis kBTFI058 02647879 FNQ LFN atropygialis kBTFI059 02647880 FNQ LFN atropygialis kBTFI060 02647881 FNQ LFN atropygialis kBTFI061 02647882 FNQ LFN atropygialis kBTFI062 02647886 FNQ LFN atropygialis kBTFI063 02647887 FNQ LFN atropygialis kBTFI064 02647888 FNQ LFN atropygialis kBTFI065 02647890 FNQ LFN atropygialis kBTFI066 02647891 FNQ LFN atropygialis kBTFI067 02647892 FNQ LFN atropygialis kBTFI068 02647893 FNQ LFN atropygialis kBTFI069 02647895 FNQ LFN atropygialis kBTFI070 02647896 FNQ LFN atropygialis kBTFI071 02647897 FNQ LFN atropygialis kBTFI072 02647898 FNQ LFN atropygialis kBTFI073 02647947 TSV LAP cincta kBTFI074 02647948 TSV LAP cincta kBTFI075 02647949 TSV LAP cincta kBTFI076 02647950 TSV LAP cincta kBTFI077 02647951 TSV LAP cincta kBTFI078 02647952 TSV LAP cincta kBTFI079 02647953 TSV LAP cincta kBTFI080 02647954 TSV LAP cincta kBTFI081 02647955 TSV LAP cincta kBTFI082 02647956 TSV LAP cincta kBTFI083 02647957 TSV LAP cincta kBTFI084 02647958 TSV LAP cincta kBTFI085 02647959 TSV LAP cincta kBTFI086 02647960 TSV LAP cincta kBTFI087 02647961 TSV LAP cincta kBTFI088 02647962 TSV LAP cincta kBTFI089 02647963 TSV LAP cincta kBTFI090 02647964 TSV LAP cincta kBTFI091 02647965 TSV LAP cincta kBTFI092 02647966 TSV LAP cincta kBTFI093 02647967 TSV LAP cincta kBTFI094 02647968 TSV LAP cincta kBTFI095 02647969 TSV LAP cincta kBTFI096 02647970 TSV LAP cincta wBTFI001 02692503 TSV CLW cincta wBTFI002 02692534 TSV CLW cincta wBTFI003 02692567 TSV DOD cincta wBTFI004 02692568 TSV DOD cincta wBTFI005 02692569 TSV DOD cincta
152
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES wBTFI006 02692570 TSV DOD cincta wBTFI007 02692571 TSV DOD cincta wBTFI008 02692572 TSV DOD cincta wBTFI009 02692573 TSV CLW cincta wBTFI010 02692574 TSV CLW cincta wBTFI011 02692575 TSV CLW cincta wBTFI012 02692576 TSV CLW cincta wBTFI013 02692577 TSV CLW cincta wBTFI014§ Not banded TSV CLW cincta wBTFI015 02692590 TSV ANC cincta wBTFI016 02692591 TSV ANC cincta wBTFI017 02692592 TSV ANC cincta wBTFI018 02692594 TSV ANC cincta wBTFI019 02692595 TSV ANC cincta wBTFI020 02692670 TSV MAD cincta wBTFI021 02692671 TSV MAD cincta wBTFI022 02692672 TSV MAD cincta wBTFI023 02692673 TSV MAD cincta wBTFI024 02692674 TSV FOD cincta wBTFI025 02692675 TSV FOD cincta wBTFI026 02692676 TSV FOD cincta wBTFI027 02692677 TSV FOD cincta wBTFI028 02692678 TSV FOD cincta wBTFI029 02692679 TSV FOD cincta wBTFI030 02692680 TSV FOD cincta wBTFI031 02692681 TSV FOD cincta wBTFI032 02692682 TSV FOD cincta wBTFI033 02692683 TSV FOD cincta wBTFI034 02692684 TSV FOD cincta wBTFI035 02692685 TSV FOD cincta wBTFI036 02692686 TSV FOD cincta wBTFI037 02692687 TSV FOD cincta wBTFI038 02692688 TSV FOD cincta wBTFI039 02692689 TSV FOD cincta wBTFI040 02692690 TSV FOD cincta wBTFI041 02692691 TSV FOD cincta wBTFI042 02692692 TSV FOD cincta wBTFI043 02692693 TSV FOD cincta wBTFI044 02692694 TSV FOD cincta wBTFI045 02692695 TSV FOD cincta wBTFI046 02692696 TSV FOD cincta wBTFI047 02692697 TSV FOD cincta wBTFI048 02692698 TSV FOD cincta
§ This individual was not ringed due to a deformed leg, which wasn’t suitable for ringing
153
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES wBTFI049 02692700 TSV FOD cincta wBTFI050 02692701 TSV FOD cincta wBTFI051 02692702 TSV FOD cincta wBTFI052 02692703 TSV FOD cincta wBTFI053 02692704 TSV FOD cincta wBTFI054 not banded TSV FOD cincta wBTFI055 02692706 TSV FOD cincta wBTFI056 02692707 TSV FOD cincta wBTFI057 02692708 TSV FOD cincta wBTFI058 02692709 TSV FOD cincta wBTFI059 02692710 TSV FOD cincta wBTFI060 02692713 TSV FOD cincta wBTFI061 02692714 TSV FOD cincta wBTFI062 02692715 TSV FOD cincta wBTFI063 02692716 TSV FOD cincta wBTFI064 02692721 TSV CLW cincta wBTFI065 02692722 TSV CLW cincta wBTFI066 02692723 TSV CLW cincta wBTFI067 02692724 TSV CLW cincta wBTFI068 02692725 TSV CLW cincta wBTFI069 02692726 TSV CLW cincta wBTFI070 02692727 TSV CLW cincta wBTFI071 02692728 TSV CLW cincta wBTFI072 02692729 TSV CLW cincta wBTFI073 02692730 TSV CLW cincta wBTFI074 02692732 TSV DOD cincta wBTFI075 02692733 TSV DOD cincta wBTFI076 02692735 TSV CLW cincta wBTFI077 02692736 TSV CLW cincta wBTFI078 02692737 TSV CLW cincta wBTFI079 02692738 TSV CLW cincta wBTFI080 02692739 TSV CLW cincta wBTFI081 02692740 TSV CLW cincta wBTFI082 02692741 TSV CLW cincta wBTFI083 02692748 TSV FOD cincta wBTFI084 02692749 TSV FOD cincta wBTFI085 02692750 TSV FOD cincta wBTFI086 02692751 TSV FOD cincta wBTFI087 02692752 TSV FOD cincta wBTFI088 02692753 TSV FOD cincta wBTFI089 02692754 TSV FOD cincta wBTFI090 02692755 TSV FOD cincta wBTFI091 02692756 TSV FOD cincta wBTFI092 02692757 CEQ DES cincta wBTFI093 02692758 CEQ DES cincta wBTFI094 02692759 CEQ DES cincta wBTFI095 02692760 CEQ DES cincta
154
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES wBTFI096 02692762 CEQ DES cincta wBTFI097 02692763 CEQ DES cincta wBTFI098 02692764 CEQ DES cincta wBTFI099 02692765 CEQ DES cincta wBTFI100 02692766 CEQ DES cincta wBTFI101 02692767 CEQ DES cincta wBTFI102 02692768 CEQ DES cincta wBTFI103 02692769 CEQ DES cincta wBTFI104 02692770 CEQ DES cincta wBTFI105 02692771 CEQ DES cincta wBTFI106 02692772 CEQ DES cincta wBTFI107 02692773 CEQ DES cincta wBTFI108 02692774 CEQ DES cincta wBTFI109 02692775 CEQ DES cincta wBTFI110 02692776 CEQ DES cincta wBTFI111 02692777 CEQ DES cincta wBTFI112 02692778 CEQ DES cincta wBTFI113 02692779 CEQ DES cincta wBTFI114 02692780 CEQ DES cincta wBTFI115 02692782 CEQ DES cincta wBTFI116 02692783 CEQ DES cincta wBTFI117 02692784 CEQ DES cincta wBTFI118 02692785 CEQ DES cincta wBTFI119 02692786 CEQ DES cincta wBTFI120 02692787 CEQ DES cincta wBTFI121 02692788 CEQ DES cincta wBTFI122 02692789 CEQ DES cincta wBTFI123 02692790 CEQ DES cincta wBTFI124 02692791 CEQ DES cincta wBTFI125 02692792 CEQ DES cincta wBTFI126 02692793 CEQ DES cincta wBTFI127 02692794 CEQ DES cincta wBTFI128 02692795 CEQ DES cincta wBTFI129 02692796 CEQ DES cincta wBTFI130 02692797 CEQ DES cincta wBTFI131 02692798 CEQ DES cincta wBTFI132 02692799 CEQ DES cincta wBTFI133 02692800 CEQ DES cincta wBTFI134 02692801 CEQ DES cincta wBTFI135 02692802 CEQ DES cincta wBTFI136 02692813 FNQ MAW atropygialis wBTFI137 02692815 FNQ MAW atropygialis wBTFI138 02692816 FNQ MAW atropygialis wBTFI139 02692817 FNQ MAW atropygialis wBTFI140 02692818 FNQ MAW atropygialis wBTFI141 02692819 FNQ MAW atropygialis wBTFI142 02692820 FNQ MAW atropygialis
155
SAMPLE ID RING CODE‡ REGION SITE SUBSPECIES wBTFI143 02692821 FNQ MAW atropygialis wBTFI144 02692822 FNQ MAW atropygialis wBTFI145 02692823 FNQ MAW atropygialis wBTFI146 02692831 FNQ MAW atropygialis wBTFI147 02692833 FNQ MAW atropygialis wBTFI148 02692834 FNQ MAW atropygialis wBTFI149 02692835 FNQ MAW atropygialis wBTFI150 02692838 FNQ MAW atropygialis wBTFI151 02692841 FNQ MAW atropygialis wBTFI152 02692849 TSV ARC cincta wBTFI153 02692850 TSV ARC cincta wBTFI154 02692851 TSV ARC cincta wBTFI155 02692852 TSV ARC cincta wBTFI156 02692853 TSV ARC cincta
156
Raw Data II: details of the Black-throated Finch samples acquired from
the captivity used in the analysis
Origin codes:
SEQ: south-eastern Queensland
NSW: northern New South Wales
ROC: Rockhampton, central QLD
AYR: Ayr, northern Queensland
UNK: origin unknown
Source codes:
QLDa – QLDe: five breeders from Queensland denoted as a – e
NSWa – NSWe: five breeders from New South Wales denoted as a – e
SAMPLE ID ORIGIN SOURCE SUBSPECIES cBTFI001 SEQ QLDa cincta cBTFI002 SEQ QLDa cincta cBTFI003 SEQ QLDa cincta cBTFI004 SEQ QLDa cincta cBTFI005 SEQ QLDa cincta cBTFI006 SEQ QLDa cincta cBTFI007 SEQ QLDa cincta cBTFI008 SEQ QLDa cincta cBTFI009 SEQ NSWa cincta cBTFI010 SEQ NSWb cincta cBTFI011 SEQ NSWb cincta cBTFI012 SEQ NSWb cincta cBTFI013 SEQ NSWa cincta cBTFI014 SEQ NSWb cincta cBTFI015 SEQ NSWb cincta cBTFI016 SEQ NSWc cincta cBTFI017 SEQ NSWb cinctacBTFI018 SEQ NSWb cincta
157
SAMPLE ID ORIGIN SOURCE SUBSPECIES cBTFI019 SEQ NSWb cincta cBTFI020 NSW NSWd cincta cBTFI021 NSW NSWd cincta cBTFI022 SEQ NSWd cincta cBTFI023 SEQ NSWd cincta cBTFI024 UNK NSWd cincta cBTFI025 SEQ NSWd cincta cBTFI026 UNK NSWd cincta cBTFI027 UNK NSWd cincta cBTFI028 AYR QLDb cincta cBTFI029 AYR QLDb cincta cBTFI030 AYR QLDb cincta cBTFI031 AYR QLDb cincta cBTFI032 AYR QLDb cincta cBTFI033 AYR QLDb cincta cBTFI034 UNK QLDc cincta cBTFI035 UNK QLDc cincta cBTFI036 UNK QLDc cincta cBTFI037 UNK QLDc cincta cBTFI038 UNK QLDc cincta cBTFI039 UNK QLDc cincta cBTFI040 UNK QLDc cincta cBTFI041 UNK QLDc cincta cBTFI042 UNK QLDc cincta cBTFI043 UNK QLDc cincta cBTFI044 UNK QLDc cincta cBTFI045 UNK QLDc cincta cBTFI046 UNK QLDc cincta cBTFI047 UNK QLDc cincta cBTFI048 UNK QLDc cincta cBTFI049 UNK QLDc cincta cBTFI050 UNK QLDd unknown** cBTFI051 UNK QLDd unknown cBTFI052 UNK QLDd unknown cBTFI053 UNK QLDd unknown cBTFI054 UNK QLDd unknown cBTFI055 UNK QLDd unknown cBTFI056 UNK QLDe unknown cBTFI057 UNK QLDe unknown cBTFI058 UNK QLDe unknown cBTFI059 UNK QLDe unknown cBTFI060 UNK NSWc cincta
** These individuals showed mixed rump colours and they were the offspring of both subspecies.
158
SAMPLE ID ORIGIN SOURCE SUBSPECIES cBTFI061 UNK NSWc cincta cBTFI062 UNK NSWc cincta cBTFI063 UNK NSWc cincta cBTFI064 UNK NSWc cincta cBTFI065 UNK NSWc cincta cBTFI066 UNK NSWc cincta cBTFI067 UNK NSWc cincta cBTFI068 UNK NSWc cincta cBTFI069 UNK NSWc cincta cBTFI070 UNK NSWc cincta cBTFI071 UNK NSWc cincta cBTFI072 UNK NSWc cincta cBTFI073 UNK NSWc cincta cBTFI074 UNK NSWc cincta cBTFI075 UNK NSWc cincta cBTFI076 UNK NSWc cincta cBTFI077 UNK NSWc cincta cBTFI078 UNK NSWc cincta cBTFI079 UNK NSWc cincta cBTFI080 ROC NSWe cincta cBTFI081 ROC NSWe cincta cBTFI082 ROC NSWe cincta cBTFI083 ROC NSWe cincta cBTFI084 ROC NSWe cincta cBTFI085 ROC NSWe cincta cBTFI086 ROC NSWe cincta cBTFI087 ROC NSWe cincta cBTFI088 ROC NSWe cincta cBTFI089 ROC NSWe cincta cBTFI090 ROC NSWe cincta cBTFI091 ROC NSWe cincta cBTFI092 ROC NSWe cincta cBTFI093 ROC NSWe cincta cBTFI094 ROC NSWa cincta cBTFI095 ROC NSWa cincta cBTFI096 ROC NSWa cincta cBTFI097 ROC NSWa cincta